My recent work on google scholar.

My most cited work on google scholar.

ArXiV Preprint list.

A list ordered by **type of publication** is in my in CV.

Contact me if you need a copy of a paper.

### 2017

- J. Lorenz, “Modeling the Evolution of Ideological Landscapes through Opinion Dynamics,” in Advances in Social Simulation 2015, 2017.
`@InProceedings{Lorenz2015ModelingEvolutionIdeological, Title = {Modeling the Evolution of Ideological Landscapes through Opinion Dynamics}, Author = {Lorenz, Jan}, Booktitle = {Advances in Social Simulation 2015}, Year = {2017}, Note = {available in Spring 2017}, Publisher = {Springer}, Owner = {janlo}, Timestamp = {2016.05.26} }`

- J. Lorenz, “Source files and scripts of the Habilitation Work submitted to Jacobs University Bremen,” , 2017.
`@Other{Lorenz2017Sourcefilesand, Title = {Source files and scripts of the Habilitation Work submitted to Jacobs University Bremen}, Author = {Jan Lorenz}, Doi = {10.5281/zenodo.238807}, Note = {Provided at github.com/janlorenz/habil archived under doi:10.5281/zenodo.238807}, Owner = {janlo}, Timestamp = {2016.11.17}, Year = {2017} }`

- J. Lorenz, F. Paetzel, and M. S. Tepe, “Just Don’t Call it a Tax! Framing in an Experiment on Voting and Redistribution,” Journal of Experimental Political Science, 2017.
`@Article{Lorenz.Paetzel.ea2017JustDontCall, Title = {Just Don't Call it a Tax! Framing in an Experiment on Voting and Redistribution}, Author = {Lorenz, Jan and Paetzel, Fabian and Tepe, Markus S.}, Journal = {Journal of Experimental Political Science}, Year = {2017}, Note = {to appear March 2017, preprint available at SSRN}, Doi = {10.2139/ssrn.2611534}, Owner = {janlo}, Timestamp = {2015.06.30} }`

### 2016

- G. Dragolov, Z. S. IgnÃ¡cz, J. Lorenz, J. Delhey, K. Boehnke, and K. Unzicker, Social Cohesion in the Western World: What Holds Societies Together: Insights from the Social Cohesion Radar, Springer, 2016.
`@Book{Dragolov.Ignacz.ea2016SocialCohesionin, Title = {Social Cohesion in the Western World: What Holds Societies Together: Insights from the Social Cohesion Radar}, Author = {Dragolov, Georgi and Ign\'acz, Zs\'ofia S. and Lorenz, Jan and Delhey, Jan and Boehnke, Klaus and Unzicker, Kai}, Publisher = {Springer}, Year = {2016}, Series = {SpringerBriefs in Well-Being and Quality of Life Research}, Doi = {10.1007/978-3-319-32464-7}, Owner = {janlo}, Timestamp = {2016.07.13} }`

- M. D. KÃ¶nig, J. Lorenz, and F. Zilibotti, “Innovation vs. imitation and the evolution of productivity distributions,” Theoretical Economics, vol. 11, iss. 3, pp. 1053-1102, 2016.
`@Article{Koenig.Lorenz.ea2016Innovationvsimitation, Title = {Innovation vs. imitation and the evolution of productivity distributions}, Author = {K\"onig, Michael D. and Lorenz, Jan and Zilibotti, Fabrizio}, Journal = {Theoretical Economics}, Year = {2016}, Number = {3}, Pages = {1053-1102}, Volume = {11}, Owner = {janlo}, Timestamp = {2017.01.23}, Url = {https://econtheory.org/ojs/index.php/te/article/view/20161053/0} }`

- T. Kurahashi-Nakamura, M. MÃ¤s, and J. Lorenz, “Robust Clustering in Generalized Bounded Confidence Models,” Journal of Artificial Societies and Social Simulation, vol. 19, iss. 4, p. 7, 2016. [Abstract]

Bounded confidence models add a critical theoretical ingredient to the explanation of opinion clustering, opinion polarisation, and the persistence of opinion diversity, assuming that individuals are only influenced by others who are sufficiently similar and neglect actors with too different views. However, despite its enormous recognition in the literature, the bounded confidence assumption has been criticized for being able to explain diversity only when implemented in a very strict and unrealistic way. The model is unable to explain patterns of opinion diversity when actors are sometimes influenced also by others who hold distant views, even when these deviations from the bounded-confidence assumption are rare and random. Here, we echo this criticism but we also show that the model’s ability to explain opinion diversity can be regained when another assumption is relaxed. Building on modeling work from statistical mechanics, we include that actors’ opinion changes do not only result from social influence. When other influences are modelled as random, uniformly distributed draws, then robust patterns of opinion clustering emerge also with the relaxed implementations of bounded confidence. The results holds under both communication regimes: the updating to the average of all acceptable opinions as in the model of Hegselmann and Krause (2002) and random pair-wise communication as in the model of Deffuant et al. (2000). We discuss implications for future modelling work and point to gaps in empirical research on influence.

`@Article{KurahashiNakamura.Maes.ea2016RobustClusteringin, Title = {Robust Clustering in Generalized Bounded Confidence Models}, Author = {Kurahashi-Nakamura, Takasumi and M\"{a}s, Michael and Lorenz, Jan}, Journal = {Journal of Artificial Societies and Social Simulation}, Year = {2016}, Number = {4}, Pages = {7}, Volume = {19}, Abstract = {Bounded confidence models add a critical theoretical ingredient to the explanation of opinion clustering, opinion polarisation, and the persistence of opinion diversity, assuming that individuals are only influenced by others who are sufficiently similar and neglect actors with too different views. However, despite its enormous recognition in the literature, the bounded confidence assumption has been criticized for being able to explain diversity only when implemented in a very strict and unrealistic way. The model is unable to explain patterns of opinion diversity when actors are sometimes influenced also by others who hold distant views, even when these deviations from the bounded-confidence assumption are rare and random. Here, we echo this criticism but we also show that the model's ability to explain opinion diversity can be regained when another assumption is relaxed. Building on modeling work from statistical mechanics, we include that actors' opinion changes do not only result from social influence. When other influences are modelled as random, uniformly distributed draws, then robust patterns of opinion clustering emerge also with the relaxed implementations of bounded confidence. The results holds under both communication regimes: the updating to the average of all acceptable opinions as in the model of Hegselmann and Krause (2002) and random pair-wise communication as in the model of Deffuant et al. (2000). We discuss implications for future modelling work and point to gaps in empirical research on influence.}, Doi = {10.18564/jasss.3220}, ISSN = {1460-7425}, Keywords = {Opinion Dynamics, Continuous Opinions, Noise, Diversity Puzzle, Facilitation, Probability of Acceptance}, Owner = {janlo}, Timestamp = {2016.10.17}, Url = {http://jasss.soc.surrey.ac.uk/19/4/7.html} }`

- J. Lorenz, C. Brauer, and D. Lorenz, “Rank-Optimal Weighting or “How to be Best in the OECD Better Life Index?”,” Social Indicators Research, vol. Online First, 2016. [Abstract]

We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector which brings it as far up in the ranking as possible with the greatest advance of the immediate rivals. The method is able to answer the question “What is the best possible rank a country can achieve with a given set of weighted indicators?” Typically, weights in composite indicators are justified normatively and not empirically. Our approach helps to give bounds on what is achievable by such normative judgments from a purely output-oriented and strongly competitive perspective. The method can serve as a basis for exact bounds in sensitivity analysis focused on ranking positions. In the OECD Better Life Index data we find that 19 out the 36 countries in the OECD Better Life Index 2014 can be brought to the top of the ranking by specific weights. We give a table of weights for each country which brings it to its highest possible position. Many countries achieve their best rank by focusing on their strong dimensions and setting the weights of many others to zero. Although setting dimensions to zero is possible in the OECD’s online tool, this contradicts the idea of better life being multidimensional in essence. We discuss modifications of the optimization problem which could take this into account, e.g. by allowing only a minimal weight of one. Methods to find rank-optimal weights can be useful for various multidimensional datasets like the ones used to rank universities or employers.

`@Article{Lorenz.Brauer.ea2016RankOptimalWeighting, Title = {Rank-Optimal Weighting or "How to be Best in the OECD Better Life Index?"}, Author = {Jan Lorenz and Christoph Brauer and Dirk Lorenz}, Journal = {Social Indicators Research}, Year = {2016}, Volume = {Online First}, Abstract = {We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector which brings it as far up in the ranking as possible with the greatest advance of the immediate rivals. The method is able to answer the question "What is the best possible rank a country can achieve with a given set of weighted indicators?" Typically, weights in composite indicators are justified normatively and not empirically. Our approach helps to give bounds on what is achievable by such normative judgments from a purely output-oriented and strongly competitive perspective. The method can serve as a basis for exact bounds in sensitivity analysis focused on ranking positions. In the OECD Better Life Index data we find that 19 out the 36 countries in the OECD Better Life Index 2014 can be brought to the top of the ranking by specific weights. We give a table of weights for each country which brings it to its highest possible position. Many countries achieve their best rank by focusing on their strong dimensions and setting the weights of many others to zero. Although setting dimensions to zero is possible in the OECD's online tool, this contradicts the idea of better life being multidimensional in essence. We discuss modifications of the optimization problem which could take this into account, e.g. by allowing only a minimal weight of one. Methods to find rank-optimal weights can be useful for various multidimensional datasets like the ones used to rank universities or employers.}, Doi = {10.1007/s11205-016-1416-0}, Owner = {janlo}, Timestamp = {2016.09.02} }`

- J. Lorenz, T. Kurahashi-Nakamura, and M. Mäs, “ContinuousOpinionDynamicsNetLogo: Robust clustering in generalized bounded confidence models,” , 2016.
`@Electronic{Lorenz.KurahashiNakamura.ea2016ContinuousOpinionDynamicsNetLogoRobustclustering, Title = {ContinuousOpinionDynamicsNetLogo: Robust clustering in generalized bounded confidence models}, Author = {Jan Lorenz and Takasumi Kurahashi-Nakamura and Michael Mäs}, Language = {netlogo}, Month = {Aug}, Year = {2016}, Doi = {10.5281/zenodo.61205}, Owner = {janlo}, Timestamp = {2016.08.30} }`

### 2015

- J. Lorenz, C. Brauer, and D. A. Lorenz, “Rank-optimal Weighting Procedure and Replication Data for: How to be best in the OECD Better Life Index?,” , p. â€“, 2015.
`@Other{Lorenz.Brauer.ea2015RankoptimalWeighting, Title = {Rank-optimal Weighting Procedure and Replication Data for: How to be best in the OECD Better Life Index?}, Author = {Lorenz, Jan and Brauer, Christoph and Lorenz, Dirk A.}, Doi = {10.7910/DVN/TUDJHX}, Note = {V1 [UNF:6:ii6SPzm200Z9SaPfi4yOag==]}, Owner = {janlo}, Pages = {--}, Publisher = {Harvard Dataverse}, Timestamp = {2017.01.23}, Url = {http://dx.doi.org/10.7910/DVN/TUDJHX}, Year = {2015} }`

- J. Lorenz, F. Paetzel, and M. Tepe, “Experimental Data: Framing in an experiment on voting and redistribution,” , p. â€“, 2015.
`@Other{Lorenz.Paetzel.ea2015ExperimentalDataFraming, Title = {Experimental Data: Framing in an experiment on voting and redistribution}, Author = {Lorenz, Jan and Paetzel, Fabian and Tepe, Markus}, Doi = {10.7910/DVN/KYEQ7X}, Note = {Harvard Dataverse, V3 [UNF:6:bnILA1zioGAF+wEFoffltQ==]}, Owner = {janlo}, Pages = {--}, Publisher = {Harvard Dataverse}, Timestamp = {2015.07.30}, Year = {2015} }`

- J. Lorenz, H. Rauhut, and B. Kittel, “Experimental Data: Majoritarian Democracy undermines truth-finding in deliberative committees,” , 2015.
`@Other{Lorenz.Rauhut.ea2015ExperimentalDataMajoritarian, Title = {Experimental Data: Majoritarian Democracy undermines truth-finding in deliberative committees}, Author = {Lorenz, Jan and Rauhut, Heiko and Kittel, Bernhard}, Doi = {10.7910/DVN/26907}, Note = {UNF:5:LLNmZiLTHF9hnvl7dHVG+A== Harvard Dataverse Network [Distributor] V3 [Version]}, Owner = {janlo}, Publisher = {Harvard Dataverse}, Timestamp = {2017.01.23}, Url = {http://dx.doi.org/10.7910/DVN/26907}, Year = {2015} }`

- J. Lorenz, H. Rauhut, and B. Kittel, “Majoritarian Democracy Undermines Truth-Finding in Deliberative Committees,” Research & Politics, iss. 2, p. 1â€“10, 2015. [Abstract]

The median of independent judgments usually outperforms individual estimates of vaguely known facts. It is well known that communication undermines this wisdom-of-crowd effect because it makes judgments mutually dependent. Instead, according to deliberative democratic theory, the transparent and honest exchange of knowledge and opinions should promote generally acceptable and correct decisions. We study the effect of deliberation, majority rule and unanimity rule on the wisdom-of-crowd effect. We conducted a non-competitive group experiment where subjects in small deliberative committees had to communicate and finally judge vaguely known facts. Subjects’ rewards were inversely related to the distance of the true value to their individual, their majoritarian or their consensual final judgment. Taking the median of initial individual private estimates as a benchmark, the results show that after communication groups perform worse when they were to decide by majority compared to groups deciding unanimously or with individual rewards. The reason for lower collective intelligence after deliberation under majority rule is probably that epistemic motivation of individuals is low because estimates need not be taken into account by others, while at the same time social motivation to change opinion for majority’s sake is high.

`@Article{Lorenz.Rauhut.ea2015MajoritarianDemocracyUndermines, Title = {Majoritarian Democracy Undermines Truth-Finding in Deliberative Committees}, Author = {Lorenz, Jan and Rauhut, Heiko and Kittel, Bernhard}, Journal = {Research \& Politics}, Year = {2015}, Number = {2}, Pages = {1--10}, Abstract = {The median of independent judgments usually outperforms individual estimates of vaguely known facts. It is well known that communication undermines this wisdom-of-crowd effect because it makes judgments mutually dependent. Instead, according to deliberative democratic theory, the transparent and honest exchange of knowledge and opinions should promote generally acceptable and correct decisions. We study the effect of deliberation, majority rule and unanimity rule on the wisdom-of-crowd effect. We conducted a non-competitive group experiment where subjects in small deliberative committees had to communicate and finally judge vaguely known facts. Subjects' rewards were inversely related to the distance of the true value to their individual, their majoritarian or their consensual final judgment. Taking the median of initial individual private estimates as a benchmark, the results show that after communication groups perform worse when they were to decide by majority compared to groups deciding unanimously or with individual rewards. The reason for lower collective intelligence after deliberation under majority rule is probably that epistemic motivation of individuals is low because estimates need not be taken into account by others, while at the same time social motivation to change opinion for majority's sake is high.}, Doi = {10.1177/2053168015582287}, Owner = {janlo}, Timestamp = {2014.01.10} }`

### 2014

- P. Groeber, J. Lorenz, and F. Schweitzer, “Dissonance minimization as a microfoundation of social influence in models of opinion formation,” Journal of Mathematical Sociology, vol. 38, iss. 3, pp. 147-174, 2014. [Abstract]

Models of opinion formation are used to investigate many collective phenomena. While social influence often constitutes a basic mechanism, its implementation differs between the models. In this paper, we provide a general framework of social influence inspired by the concept of cognitive dissonance. We only premise that individuals strive to minimize dissonance resulting from different opinions compared to individuals in a given social network. Within a game theoretic context, we show that our concept of dissonance reduction exhibits basic properties of a coordination process. We further show that different models of opinion formation can be represented as best response dynamics within our framework. Thus, we offer a unifying perspective on these heterogeneous models and link them to rational choice theory.

`@Article{Groeber.Lorenz.ea2014Dissonanceminimizationas, Title = {Dissonance minimization as a microfoundation of social influence in models of opinion formation}, Author = {Patrick Groeber and Jan Lorenz and Frank Schweitzer}, Journal = {Journal of Mathematical Sociology}, Year = {2014}, Number = {3}, Pages = {147-174}, Volume = {38}, Abstract = {Models of opinion formation are used to investigate many collective phenomena. While social influence often constitutes a basic mechanism, its implementation differs between the models. In this paper, we provide a general framework of social influence inspired by the concept of cognitive dissonance. We only premise that individuals strive to minimize dissonance resulting from different opinions compared to individuals in a given social network. Within a game theoretic context, we show that our concept of dissonance reduction exhibits basic properties of a coordination process. We further show that different models of opinion formation can be represented as best response dynamics within our framework. Thus, we offer a unifying perspective on these heterogeneous models and link them to rational choice theory.}, Doi = {10.1080/0022250X.2012.724486}, Jlprojects = {continuous_opinion_dynamics, evolution_of_norms}, Owner = {janlo}, Timestamp = {2011.03.08} }`

- J. Lorenz, “How Clustered Ideological Landscapes Emerge Through Opinion Dynamics,” , 2014.
`@Article{Lorenz2014HowClusteredIdeological, Title = {How Clustered Ideological Landscapes Emerge Through Opinion Dynamics}, Author = {Jan Lorenz}, Year = {2014}, Journaltitle = {ECPR General Conference Glasgow}, Notes = {Panel: Preference Formation and Formal Models of Politics}, Owner = {janlo}, Timestamp = {2014.10.27}, Url = {http://ecpr.eu/Events/PaperDetails.aspx?PaperID=20737&EventID=14} }`

### 2013

- J. Lorenz, F. Paetzel, and F. Schweitzer, “Redistribution spurs growth by using a portfolio effect on risky human capital,” PLoS One, vol. 8, iss. 2, p. e54904, 2013. [Abstract]

We demonstrate by mathematical analysis and systematic computer simulations that taxation and redistribution of wealth can lead to sustainable growth of wealth in a society. The wealth dynamics of each agent is described by a stochastic multiplicative process which, in the long run, leads to the destruction of individual wealth and the extinction of the individualistic society. When agents are coupled by redistributive taxation the situation might turn to individual growth in the long run. We consider that a government collected a proportion of wealth and reduces it by a fraction as costs for administration. The remaining public good is equally redistributed to all agents. We derive conditions under which the destruction of wealth can be turned into sustainable growth, despite the losses from the random growth process and despite the administrative costs. The findings are verified for three different tax schemes: proportional tax, taking proportional more from the rich, and proportionally more from the poor. We discuss which of these tax schemes is optimal with respect to maximize growth of wealth under a fixed rate of administrative costs, or with respect to maximize the governmental income. This leads us to some general conclusions about governmental decisions, the relation to public good games, and the use of taxation in a risk taking society.

`@Article{Lorenz.Paetzel.ea2013Redistributionspursgrowth, Title = {Redistribution spurs growth by using a portfolio effect on risky human capital}, Author = {Jan Lorenz and Fabian Paetzel and Frank Schweitzer}, Journal = {PLoS One}, Year = {2013}, Number = {2}, Pages = {e54904}, Volume = {8}, Abstract = {We demonstrate by mathematical analysis and systematic computer simulations that taxation and redistribution of wealth can lead to sustainable growth of wealth in a society. The wealth dynamics of each agent is described by a stochastic multiplicative process which, in the long run, leads to the destruction of individual wealth and the extinction of the individualistic society. When agents are coupled by redistributive taxation the situation might turn to individual growth in the long run. We consider that a government collected a proportion of wealth and reduces it by a fraction as costs for administration. The remaining public good is equally redistributed to all agents. We derive conditions under which the destruction of wealth can be turned into sustainable growth, despite the losses from the random growth process and despite the administrative costs. The findings are verified for three different tax schemes: proportional tax, taking proportional more from the rich, and proportionally more from the poor. We discuss which of these tax schemes is optimal with respect to maximize growth of wealth under a fixed rate of administrative costs, or with respect to maximize the governmental income. This leads us to some general conclusions about governmental decisions, the relation to public good games, and the use of taxation in a risk taking society.}, Doi = {10.1371/journal.pone.0054904}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2011.05.17} }`

- T. Metz and J. Lorenz, “Become who you are: The homing pattern in partisanship as a self-reinforcing stochastic process,” SSRN Preprint, 2013. [Abstract]

Partisanship is characterized by a homing pattern in which individuals pick a party and alternate between supporting and not supporting it. We present a stochastic model in which the probability to announce an attachment depends on an initial propensity to support the party and the number of times support has been announced before. The model reproduces the empirical distributions of the total number of utterances of party preferences in German panel data holding the longest and most dense measurement of Partisanship worldwide.

`@Article{Metz.Lorenz2013Becomewhoyou, Title = {Become who you are: The homing pattern in partisanship as a self-reinforcing stochastic process}, Author = {Metz, Thomas and Lorenz, Jan}, Journal = {SSRN Preprint}, Year = {2013}, Abstract = {Partisanship is characterized by a homing pattern in which individuals pick a party and alternate between supporting and not supporting it. We present a stochastic model in which the probability to announce an attachment depends on an initial propensity to support the party and the number of times support has been announced before. The model reproduces the empirical distributions of the total number of utterances of party preferences in German panel data holding the longest and most dense measurement of Partisanship worldwide.}, Owner = {janlo}, Timestamp = {2013.03.05}, Url = {http://ssrn.com/abstract=2224740} }`

### 2012

- J. Lorenz, “Zur Methode der agenten-basierten Simulation in der Politikwissenschaft am Beispiel von Meinungsdynamik und Parteienwettstreit,” in Jahrbuch fÃ¼r Handlungs- und Entscheidungstheorie. Band 7: Experiment und Simulation, T. BrÃ¤uninger, A. BÃ¤chtiger, and S. Shikano, Eds., VS Verlag fÃ¼r Sozialwissenschaften, 2012, p. 31â€“58.
`@InCollection{Lorenz2012ZurMethodeder, Title = {{Zur Methode der agenten-basierten Simulation in der Politikwissenschaft am Beispiel von Meinungsdynamik und Parteienwettstreit}}, Author = {Jan Lorenz}, Booktitle = {Jahrbuch f\"ur Handlungs- und Entscheidungstheorie. Band 7: Experiment und Simulation}, Publisher = {VS Verlag f\"ur Sozialwissenschaften}, Year = {2012}, Editor = {Thomas Br\"auninger and Andr\'e B\"achtiger and Susumu Shikano}, Pages = {31--58}, Doi = {10.1007/978-3-531-19606-0_2}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2012.01.24} }`

- J. Lorenz, “Netlogo Model: Continuous Opinion Dynamics under Bounded Confidence,” NetLogo User Community Models, 2012.
`@Article{Lorenz2012ContinuousOpinionDynamics, Title = {Netlogo Model: Continuous Opinion Dynamics under Bounded Confidence}, Author = {Jan Lorenz}, Journal = {NetLogo User Community Models}, Year = {2012}, Note = {See also \url{http://janlo.de/applets/bc.html}.}, Owner = {janlo}, Timestamp = {2013.03.10}, Url = {http://ccl.northwestern.edu/netlogo/models/community/bc} }`

### 2011

- J. Lorenz, H. Rauhut, F. Schweitzer, and D. Helbing, “How social influence can undermine the wisdom of crowd effect,” Proceedings of the National Academy of Sciences, vol. 108, iss. 22, p. 9020â€“9025, 2011. [Abstract]

Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects’ convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others’ responses was provided. Although groups are initially ”wise,” knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The ”social influence effect” diminishes the diversity of the crowd without improvements of its collective error. The ”range reduction effect” moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The ”confidence effect” boosts individuals’ confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.

`@Article{Lorenz.Rauhut.ea2011HowSocialInfluence, Title = {How social influence can undermine the wisdom of crowd effect}, Author = {Lorenz, Jan and Rauhut, Heiko and Schweitzer, Frank and Helbing, Dirk}, Journal = {Proceedings of the National Academy of Sciences}, Year = {2011}, Number = {22}, Pages = {9020--9025}, Volume = {108}, Abstract = {Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects' convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others' responses was provided. Although groups are initially ''wise,'' knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The ''social influence effect'' diminishes the diversity of the crowd without improvements of its collective error. The ''range reduction effect'' moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The ''confidence effect'' boosts individuals' confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.}, Doi = {10.1073/pnas.1008636108}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2011.04.13} }`

- H. Rauhut, J. Lorenz, F. Schweitzer, and D. Helbing, “Reply to Farrell: Improved individual estimation success can imply collective tunnel vision,” Proceedings of the National Academy of Sciences, vol. 108, iss. 36, p. E626, 2011.
`@Article{Rauhut.Lorenz.ea2011ReplytoFarrell, Title = {Reply to {Farrell}: Improved individual estimation success can imply collective tunnel vision}, Author = {Rauhut, Heiko and Lorenz, Jan and Schweitzer, Frank and Helbing, Dirk}, Journal = {Proceedings of the National Academy of Sciences}, Year = {2011}, Number = {36}, Pages = {E626}, Volume = {108}, Doi = {10.1073/pnas.1111007108}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2011.09.07} }`

### 2010

- J. Lorenz, “Heterogeneous bounds of confidence: Meet, discuss and find consensus!,” Complexity, vol. 15, iss. 4, p. 43â€“52, 2010. [Abstract]

Models of continuous opinion dynamics under bounded confidence show a sharp transition between a consensus and a polarization phase at a critical global bound of confidence. In this paper, heterogeneous bounds of confidence are studied. The surprising result is that a society of agents with two different bounds of confidence (open-minded and closed-minded agents) can find consensus even when both bounds of confidence are significantly below the critical bound of confidence of a homogeneous society. The phenomenon is shown by examples of agent-based simulation and by numerical computation of the time evolution of the agents density. The result holds for the bounded confidence model of Deffuant, Weisbuch and others (Weisbuch, G. et al; Meet, discuss, and segregate!, Complexity, 2002, 7, 55â€“63), as well as for the model of Hegselmann and Krause (Hegselmann, R., Krause, U.; Opinion Dynamics and Bounded Confidence, Models, Analysis and Simulation, Journal of Artificial Societies and Social Simulation, 2002, 5, 2). Thus, given an average level of confidence, diversity of bounds of confidence enhances the chances for consensus. The drawback of this enhancement is that opinion dynamics becomes suspect to severe drifts of clusters, where open-minded agents can pull closed-minded agents towards another cluster of closed-minded agents. A final consensus might thus not lie in the center of the opinion interval as it happens for uniform initial opinion distributions under homogeneous bounds of confidence. It can be located at extremal locations. This is demonstrated by example. This also show that the extension to heterogeneous bounds of confidence enriches the complexity of the dynamics tremendously.

`@Article{Lorenz2010Heterogeneousboundsof, Title = {Heterogeneous bounds of confidence: Meet, discuss and find consensus!}, Author = {Jan Lorenz}, Journal = {Complexity}, Year = {2010}, Number = {4}, Pages = {43--52}, Volume = {15}, Abstract = {Models of continuous opinion dynamics under bounded confidence show a sharp transition between a consensus and a polarization phase at a critical global bound of confidence. In this paper, heterogeneous bounds of confidence are studied. The surprising result is that a society of agents with two different bounds of confidence (open-minded and closed-minded agents) can find consensus even when both bounds of confidence are significantly below the critical bound of confidence of a homogeneous society. The phenomenon is shown by examples of agent-based simulation and by numerical computation of the time evolution of the agents density. The result holds for the bounded confidence model of Deffuant, Weisbuch and others (Weisbuch, G. et al; Meet, discuss, and segregate!, Complexity, 2002, 7, 55--63), as well as for the model of Hegselmann and Krause (Hegselmann, R., Krause, U.; Opinion Dynamics and Bounded Confidence, Models, Analysis and Simulation, Journal of Artificial Societies and Social Simulation, 2002, 5, 2). Thus, given an average level of confidence, diversity of bounds of confidence enhances the chances for consensus. The drawback of this enhancement is that opinion dynamics becomes suspect to severe drifts of clusters, where open-minded agents can pull closed-minded agents towards another cluster of closed-minded agents. A final consensus might thus not lie in the center of the opinion interval as it happens for uniform initial opinion distributions under homogeneous bounds of confidence. It can be located at extremal locations. This is demonstrated by example. This also show that the extension to heterogeneous bounds of confidence enriches the complexity of the dynamics tremendously.}, Collaborationtags = {JL publicationlist}, Doi = {10.1002/cplx.20295}, Eprint = {0801.1399}, Eprinttype = {arxiv}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26} }`

- J. Lorenz and D. A. Lorenz, “On Conditions for Convergence to Consensus,” IEEE Transactions on Automatic Control, vol. 55, iss. 7, p. 1651â€“1656, 2010. [Abstract]

A new theorem on conditions for convergence to consensus of a multiagent time-dependent time-discrete dynamical system is presented. The theorem is build up on the notion of averaging maps. We compare this theorem to Moreau’s Theorem and his proposed set-valued Lyapunov theory (IEEE Transactions on Automatic Control, vol. 50, no. 2, 2005). We give examples that point out differences of approaches including examples where Moreau’s theorem is not applicable but ours is. Further on, we give examples that demonstrate that the theory of convergence to consensus is still not complete.

`@Article{Lorenz.Lorenz2010ConditionsConvergenceto, Title = {On Conditions for Convergence to Consensus}, Author = {Jan Lorenz and Dirk A. Lorenz}, Journal = {IEEE Transactions on Automatic Control}, Year = {2010}, Number = {7}, Pages = {1651--1656}, Volume = {55}, Abstract = {A new theorem on conditions for convergence to consensus of a multiagent time-dependent time-discrete dynamical system is presented. The theorem is build up on the notion of averaging maps. We compare this theorem to Moreau's Theorem and his proposed set-valued Lyapunov theory (IEEE Transactions on Automatic Control, vol. 50, no. 2, 2005). We give examples that point out differences of approaches including examples where Moreau's theorem is not applicable but ours is. Further on, we give examples that demonstrate that the theory of convergence to consensus is still not complete.}, Collaborationtags = {JL publicationlist}, Doi = {10.1109/TAC.2010.2046086}, Eprint = {0803.2211}, Eprinttype = {arxiv}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26} }`

- H. Rauhut and J. Lorenz, “The wisdom of crowds in one mind: How individuals can simulate the knowledge of diverse societies to reach better decisions,” Journal of Mathematical Psychology, vol. 55, p. 191â€“197, 2010. [Abstract]

The joint knowledge of many diverse individuals can outperform experts in estimation and decision-making problems. This wisdom of the crowd has been demonstrated in different societal areas such as internet search engines, political elections or stock markets. Recently, psychologists argued that humans may even simulate a diverse society in their own mind by drawing different answers from their brain (Vul & Pashler, 2008). The underlying idea is that individuals can access different knowledge areas in their brain, whose joint evaluation yields better estimates than their separate consideration. This article presents a mathematical treatment of the wisdom of crowds and two potential mechanisms to quantify the wisdom of crowds in one mind. The implications of both methods are analyzed and applied to new experimental data (N=144), which contain five consecutive estimates from the same individuals. The theoretical and empirical analysis demonstrates limitations of the wisdom of crowds in one mind: Asking oneself several times is on average less powerful than asking only one other individual. This is due to the smaller diversity of estimates of similar individuals and the larger average bias to which they converge. Further, individuals cannot perform independent draws from an “internal distribution”. Hence, there may be other mechanisms at work such as talking oneself into believing initial guesses or eliciting progressively wilder ones.

`@Article{Rauhut.Lorenz2010wisdomofcrowds, Title = {The wisdom of crowds in one mind: How individuals can simulate the knowledge of diverse societies to reach better decisions}, Author = {Heiko Rauhut and Jan Lorenz}, Journal = {Journal of Mathematical Psychology}, Year = {2010}, Pages = {191--197}, Volume = {55}, Abstract = {The joint knowledge of many diverse individuals can outperform experts in estimation and decision-making problems. This wisdom of the crowd has been demonstrated in different societal areas such as internet search engines, political elections or stock markets. Recently, psychologists argued that humans may even simulate a diverse society in their own mind by drawing different answers from their brain (Vul \& Pashler, 2008). The underlying idea is that individuals can access different knowledge areas in their brain, whose joint evaluation yields better estimates than their separate consideration. This article presents a mathematical treatment of the wisdom of crowds and two potential mechanisms to quantify the wisdom of crowds in one mind. The implications of both methods are analyzed and applied to new experimental data (N=144), which contain five consecutive estimates from the same individuals. The theoretical and empirical analysis demonstrates limitations of the wisdom of crowds in one mind: Asking oneself several times is on average less powerful than asking only one other individual. This is due to the smaller diversity of estimates of similar individuals and the larger average bias to which they converge. Further, individuals cannot perform independent draws from an ``internal distribution''. Hence, there may be other mechanisms at work such as talking oneself into believing initial guesses or eliciting progressively wilder ones.}, Collaborationtags = {JL publicationlist}, Doi = {10.1016/j.jmp.2010.10.002}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.09.18} }`

- J. K. Shin and J. Lorenz, “Tipping Diffusivity in Information Accumulation Systems: More Links, less Consensus,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2010, iss. 06, p. P06005, 2010. [Abstract]

Assume two different communities each of which maintain their respective opinions mainly because of the weak interaction between them. In such a case, it is an interesting problem to find the necessary strength of inter-community interaction in order for the two communities to reach a consensus. In this paper, the information accumulation system (IAS) model is applied to investigate the problem. With the application of the IAS model, the opinion dynamics of the two-community problem is found to belong to a wider class of two-species problems appearing in population dynamics or in the competition of two languages, for all of which the governing equations can be described in terms of coupled logistic maps. Tipping diffusivity is defined as the maximal inter-community interaction such that the two communities maintain different opinions. For a problem with a simple community structure and homogeneous individuals, the tipping diffusivity is calculated theoretically. As a conclusion of the paper, the convergence of the two communities to the same value is less possible the more overall interaction, intra-community and inter-community, takes place. This implies, for example, that the increase in the interaction between individuals caused by the development of modern communication tools, such as Facebook and Twitter, does not necessarily improve the tendency towards global convergence between different communities. If the number of internal links increases by a factor, the number of inter-community links must be increased by an even higher factor, in order for consensus to be the only stable attractor.

`@Article{Shin.Lorenz2010TippingDiffusivityin, Title = {Tipping Diffusivity in Information Accumulation Systems: More Links, less Consensus}, Author = {Jae Kyun Shin and Jan Lorenz}, Journal = {Journal of Statistical Mechanics: Theory and Experiment}, Year = {2010}, Number = {06}, Pages = {P06005}, Volume = {2010}, Abstract = {Assume two different communities each of which maintain their respective opinions mainly because of the weak interaction between them. In such a case, it is an interesting problem to find the necessary strength of inter-community interaction in order for the two communities to reach a consensus. In this paper, the information accumulation system (IAS) model is applied to investigate the problem. With the application of the IAS model, the opinion dynamics of the two-community problem is found to belong to a wider class of two-species problems appearing in population dynamics or in the competition of two languages, for all of which the governing equations can be described in terms of coupled logistic maps. Tipping diffusivity is defined as the maximal inter-community interaction such that the two communities maintain different opinions. For a problem with a simple community structure and homogeneous individuals, the tipping diffusivity is calculated theoretically. As a conclusion of the paper, the convergence of the two communities to the same value is less possible the more overall interaction, intra-community and inter-community, takes place. This implies, for example, that the increase in the interaction between individuals caused by the development of modern communication tools, such as Facebook and Twitter, does not necessarily improve the tendency towards global convergence between different communities. If the number of internal links increases by a factor, the number of inter-community links must be increased by an even higher factor, in order for consensus to be the only stable attractor.}, Collaborationtags = {JL publicationlist}, Doi = {10.1088/1742-5468/2010/06/P06005}, Eprint = {0909.1252}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.09.08} }`

### 2009

- D. Lorenz and J. Lorenz, “Convergence to Consensus by General Averaging,” in Positive Systems, R. Bru and S. Romero-Vivo, Eds., Springer, 2009, p. 91â€“99. [Abstract]

We investigate sufficient conditions for a discrete nonlinear non-homogeneous dynamical system to converge to consensus. We formulate a theorem which is based on the notion of averaging maps. Further on, we give examples that demonstrate that the theory of convergence to consensus is still not complete.

`@InCollection{Lorenz.Lorenz2009ConvergencetoConsensus, Title = {Convergence to Consensus by General Averaging}, Author = {Lorenz, Dirk and Lorenz, Jan}, Booktitle = {Positive Systems}, Publisher = {Springer}, Year = {2009}, Editor = {Rafeal Bru and Sergion Romero-Vivo}, Pages = {91--99}, Series = {Lecture Notes in Computer Science}, Abstract = {We investigate sufficient conditions for a discrete nonlinear non-homogeneous dynamical system to converge to consensus. We formulate a theorem which is based on the notion of averaging maps. Further on, we give examples that demonstrate that the theory of convergence to consensus is still not complete.}, Doi = {10.1007/978-3-642-02894-6_9}, Journal = {Positive Systems}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.12.13} }`

- J. Lorenz, “Universality of movie rating distributions,” European Physical Journal B, vol. 71, pp. 251-258, 2009. [Abstract]

In this paper histograms of user ratings for movies (1,…,10) are analysed. The evolving stabilised shapes of histograms follow the rule that all are either double- or triple-peaked. Moreover, at most one peak can be on the central bins 2,…,9 and the distribution in these bins looks smooth `Gaussian-like’ while changes at the extremes (1 and 10) often look abrupt. It is shown that this is well approximated under the assumption that histograms are confined and discretised probability density functions of LÃ©vy skew alpha-stable distributions. These distributions are the only stable distributions which could emerge due to a generalized central limit theorem from averaging of various independent random avriables as which one can see the initial opinions of users. Averaging is also an appropriate assumption about the social process which underlies the process of continuous opinion formation. Surprisingly, not the normal distribution achieves the best fit over histograms obseved on the web, but distributions with fat tails which decay as power-laws with exponent -(1+alpha) (alpha=4/3). The scale and skewness parameters of the Levy skew alpha-stable distributions seem to depend on the deviation from an average movie (with mean about 7.6). The histogram of such an average movie has no skewness and is the most narrow one. If a movie deviates from average the distribution gets broader and skew. The skewness pronounces the deviation. This is used to construct a one parameter fit which gives some evidence of universality in processes of continuous opinion dynamics about taste.

`@Article{Lorenz2009Universalityofmovie, Title = {{Universality of movie rating distributions}}, Author = {Lorenz, Jan}, Journal = {European Physical Journal B}, Year = {2009}, Pages = {251-258}, Volume = {71}, Abstract = {In this paper histograms of user ratings for movies (1,...,10) are analysed. The evolving stabilised shapes of histograms follow the rule that all are either double- or triple-peaked. Moreover, at most one peak can be on the central bins 2,...,9 and the distribution in these bins looks smooth `Gaussian-like' while changes at the extremes (1 and 10) often look abrupt. It is shown that this is well approximated under the assumption that histograms are confined and discretised probability density functions of L\'evy skew alpha-stable distributions. These distributions are the only stable distributions which could emerge due to a generalized central limit theorem from averaging of various independent random avriables as which one can see the initial opinions of users. Averaging is also an appropriate assumption about the social process which underlies the process of continuous opinion formation. Surprisingly, not the normal distribution achieves the best fit over histograms obseved on the web, but distributions with fat tails which decay as power-laws with exponent -(1+alpha) (alpha=4/3). The scale and skewness parameters of the Levy skew alpha-stable distributions seem to depend on the deviation from an average movie (with mean about 7.6). The histogram of such an average movie has no skewness and is the most narrow one. If a movie deviates from average the distribution gets broader and skew. The skewness pronounces the deviation. This is used to construct a one parameter fit which gives some evidence of universality in processes of continuous opinion dynamics about taste.}, Collaborationtags = {JL publicationlist}, Doi = {10.1140/epjb/e2009-00283-3}, Eprint = {0806.2305}, Eprinttype = {arxiv}, Groups = {public}, Intrahash = {2661dd0ae2070f60f0f8be177aac0279}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2008.08.19} }`

- J. Lorenz, S. Battiston, and F. Schweitzer, “Systemic Risk in a Unifying Framework for Cascading Processes on Networks,” European Physical Journal B, vol. 71, p. 441â€“460, 2009. [Abstract]

We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, $X^\ast$, and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of $X^\ast$ as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class lead to a first-order phase transition in systemic risk, i.e. situations where small changes in the initial conditions may lead to a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalizations.

`@Article{Lorenz.Battiston.ea2009SystemicRiskin, Title = {Systemic Risk in a Unifying Framework for Cascading Processes on Networks}, Author = {Jan Lorenz and Stefano Battiston and Frank Schweitzer}, Journal = {European Physical Journal B}, Year = {2009}, Pages = {441--460}, Volume = {71}, Abstract = {We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, $X^\ast$, and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of $X^\ast$ as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class lead to a first-order phase transition in systemic risk, i.e. situations where small changes in the initial conditions may lead to a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalizations.}, Collaborationtags = {JL publicationlist}, Doi = {10.1140/epjb/e2009-00347-4}, Eprint = {0907.5325}, Eprinttype = {arxiv}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.09.08} }`

### 2008

- J. Lorenz, “Fostering Consensus in Multidimensional Continuous Opinion Dynamics under bounded Confidence,” in Managing Complexity: Insights, Concepts, Applications, D. Helbing, Ed., Springer, 2008, p. 321â€“334. [Abstract]

Social consensus is important for society. Sometimes the success of society depends on a consensus (e.g. the decision to pay taxes or to commit to the constitution). Examples for continuous opinion dynamics are discussions about tax rates or budget plan proposals for governments investments. Another example is a commission of experts which should reach a estimate about a certain issue, e.g. the tax revenues of the next year. In all these situations we got a group of agents which should reach a common agreement either for reaching a good approximation to the truth but on the other hand for the reason, that reaching consensus is a good in itself. From social judgment theory and experiments we know that humans either tend to agreement with others for normative and informational reasons but on the other hand have bounded confidence against others with differing opinions. In a framework of models of continuous opinion dynamics we ask, which structural conditions foster the achievement of consensus? We present evidence by simulation that bringing more issues in does, but only if the issues are under budget constraints. Further, the installation of meetings where everyone hears all opinions has a better impact than relying on gossip.

`@InCollection{Lorenz2008ManagingComplexityInsights, Title = {{Fostering Consensus in Multidimensional Continuous Opinion Dynamics under bounded Confidence}}, Author = {Jan Lorenz}, Booktitle = {Managing Complexity: Insights, Concepts, Applications}, Publisher = {Springer}, Year = {2008}, Editor = {Dirk Helbing}, Pages = {321--334}, Series = {Understanding Complex Systems}, Abstract = {Social consensus is important for society. Sometimes the success of society depends on a consensus (e.g. the decision to pay taxes or to commit to the constitution). Examples for continuous opinion dynamics are discussions about tax rates or budget plan proposals for governments investments. Another example is a commission of experts which should reach a estimate about a certain issue, e.g. the tax revenues of the next year. In all these situations we got a group of agents which should reach a common agreement either for reaching a good approximation to the truth but on the other hand for the reason, that reaching consensus is a good in itself. From social judgment theory and experiments we know that humans either tend to agreement with others for normative and informational reasons but on the other hand have bounded confidence against others with differing opinions. In a framework of models of continuous opinion dynamics we ask, which structural conditions foster the achievement of consensus? We present evidence by simulation that bringing more issues in does, but only if the issues are under budget constraints. Further, the installation of meetings where everyone hears all opinions has a better impact than relying on gossip.}, Collaborationtags = {JL publicationlist}, Doi = {10.1007/978-3-540-75261-5_15}, Eprint = {0708.3172}, Groups = {public}, Intrahash = {f7d86f5a59a3e7489041585f6c82ea2b}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2008.06.11} }`

- J. Lorenz and S. Battiston, “Systemic risk in a network fragility model analyzed with probability density evolution of persistent random walks,” Networks and Heterogeneous Media (NHM), vol. 3, iss. 2, p. 185â€“200, 2008. [Abstract]

We study the mean field approximation of a recent model of cascades on networks relevant to the investigation of systemic risk control in financial networks. In the model, the hypothesis of a trend reinforcement in the stochastic process describing the fragility of the nodes, induces a trade-off in the systemic risk with respect to the density of the network. Increasing the average link density, the network is first less exposed to systemic risk, while above an intermediate value the systemic risk increases. This result offers a simple explanation for the emergence of instabilities in financial systems that get increasingly interwoven. In this paper, we study the dynamics of the probability density function of the average fragility. This converges to a unique stable distribution which can be computed numerically and can be used to estimate the systemic risk as a function of the parameters of the model.

`@Article{Lorenz.Battiston2008Systemicriskin, Title = {{Systemic risk in a network fragility model analyzed with probability density evolution of persistent random walks}}, Author = {Lorenz, Jan and Battiston, Stefano}, Journal = {Networks and Heterogeneous Media (NHM)}, Year = {2008}, Number = {2}, Pages = {185--200}, Volume = {3}, Abstract = {We study the mean field approximation of a recent model of cascades on networks relevant to the investigation of systemic risk control in financial networks. In the model, the hypothesis of a trend reinforcement in the stochastic process describing the fragility of the nodes, induces a trade-off in the systemic risk with respect to the density of the network. Increasing the average link density, the network is first less exposed to systemic risk, while above an intermediate value the systemic risk increases. This result offers a simple explanation for the emergence of instabilities in financial systems that get increasingly interwoven. In this paper, we study the dynamics of the probability density function of the average fragility. This converges to a unique stable distribution which can be computed numerically and can be used to estimate the systemic risk as a function of the parameters of the model.}, Collaborationtags = {JL SB systemic risk, JL publicationlist}, Doi = {10.3934/nhm.2008.3.185}, Eprint = {0711.1784}, Eprinttype = {arxiv}, Groups = {public}, Intrahash = {46dd5eba2bf21a8f9656b9cdc96ff712}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2008.08.19} }`

- D. Urbig, J. Lorenz, and H. Herzberg, “Opinion dynamics: The effect of the number of peers met at once,” Journal of Artificial Societies and Social Simulation, vol. 11, iss. 2, p. 4, 2008. [Abstract]

The opinion dynamics model introduced by Deffuant and Weisbuch as well as the one by Hegselmann and Krause are rather similar. In both models individuals are assumed to have opinions about an issue, they meet and discuss, and they may adapt their opinions towards the other agents` opinions or may ignore each other if their positions are too different. Both models differ with respect to the number of peers they meet at once. Furthermore the model by Deffuant and Weisbuch has a convergence parameter that controls how fast agents adapt their opinions. By defining the reversed parameter as self-support we can extend the applicability of this parameter to scenarios with more than one interaction partner. We investigate the effects of changing the number of peers met at once, which is done for different population sizes, and the effects of changing the self-support. For describing the dynamics we look at different statistics, i.e. number of cluster, number of major clusters, and Gini coefficient.

`@Article{Urbig.Lorenz.ea2008Opiniondynamicseffect, Title = {{Opinion dynamics: The effect of the number of peers met at once}}, Author = {Urbig, Diemo and Lorenz, Jan and Herzberg, Heiko}, Journal = {Journal of Artificial Societies and Social Simulation}, Year = {2008}, Number = {2}, Pages = {4}, Volume = {11}, Abstract = {The opinion dynamics model introduced by Deffuant and Weisbuch as well as the one by Hegselmann and Krause are rather similar. In both models individuals are assumed to have opinions about an issue, they meet and discuss, and they may adapt their opinions towards the other agents` opinions or may ignore each other if their positions are too different. Both models differ with respect to the number of peers they meet at once. Furthermore the model by Deffuant and Weisbuch has a convergence parameter that controls how fast agents adapt their opinions. By defining the reversed parameter as self-support we can extend the applicability of this parameter to scenarios with more than one interaction partner. We investigate the effects of changing the number of peers met at once, which is done for different population sizes, and the effects of changing the self-support. For describing the dynamics we look at different statistics, i.e. number of cluster, number of major clusters, and Gini coefficient.}, Collaborationtags = {JL publicationlist}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {http://jasss.soc.surrey.ac.uk/11/2/4.html} }`

### 2007

- J. Lorenz, “Fixed points in models of continuous opinion dynamics under bounded confidence,” in Communications of the Laufen Colloquium on Science 2007, A. Ruffing, A. Suhrer, and J. Suhrer, Eds., Shaker Publishing, 2007. [Abstract]

We present two models of continuous opinion dynamics under bounded confidence which are representable as nonnegative discrete dynamical systems, namely the Hegselmann-Krause model (Hegselmann and Krause, Journal of Artificial Societies and Social Simulation 5(3), 2002) and the Deffuant-Weisbuch model (Deffuant et al, Advances in Complex Systems, 3, 2000). We fully characterize the set of fixed points for both models. They are identical. Further on, we present reformulations of both models on the more general level of densities of agents in the opinion space as interactive Markov chains. We also characterize the sets of fixed points as identical in both models.

`@InCollection{Lorenz2007Fixedpointsin, Title = {{Fixed points in models of continuous opinion dynamics under bounded confidence}}, Author = {Jan Lorenz}, Booktitle = {Communications of the Laufen Colloquium on Science 2007}, Publisher = {Shaker Publishing}, Year = {2007}, Editor = {A. Ruffing and A. Suhrer and J. Suhrer}, Abstract = {We present two models of continuous opinion dynamics under bounded confidence which are representable as nonnegative discrete dynamical systems, namely the Hegselmann-Krause model (Hegselmann and Krause, Journal of Artificial Societies and Social Simulation 5(3), 2002) and the Deffuant-Weisbuch model (Deffuant et al, Advances in Complex Systems, 3, 2000). We fully characterize the set of fixed points for both models. They are identical. Further on, we present reformulations of both models on the more general level of densities of agents in the opinion space as interactive Markov chains. We also characterize the sets of fixed points as identical in both models.}, Collaborationtags = {JL publicationlist}, Eprint = {0806.1587}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {http://arXiv.org/abs/0806.1587} }`

- J. Lorenz, “Continuous Opinion Dynamics under bounded confidence: A Survey,” Int. Journal of Modern Physics C, vol. 18, iss. 12, p. 1819â€“1838, 2007. [Abstract]

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines as physics, mathematics, computer science, social psychology and philosophy. Agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte-Carlo style, but they can also be reformulated on the agent’s density in the opinion space in a master-equation style. This paper is to present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence; second, to give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions; third to review the several extensions and the evolving phenomena which have been studied so far; and fourth to state some basic open questions.

`@Article{Lorenz2007ContinuousOpinionDynamics, Title = {{Continuous Opinion Dynamics under bounded confidence: A Survey}}, Author = {Lorenz, Jan}, Journal = {Int. Journal of Modern Physics C}, Year = {2007}, Number = {12}, Pages = {1819--1838}, Volume = {18}, Abstract = {Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines as physics, mathematics, computer science, social psychology and philosophy. Agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte-Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master-equation style. This paper is to present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence; second, to give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions; third to review the several extensions and the evolving phenomena which have been studied so far; and fourth to state some basic open questions.}, Collaborationtags = {JL publicationlist}, Doi = {10.1142/S0129183107011789}, Eprint = {0707.1762}, Eprinttype = {arxiv}, Groups = {public}, Intrahash = {a05731a096162209ca6acbb7b5a088c8}, Keywords = {continuous opinion dynamics, emmyown}, Owner = {janlo}, Timestamp = {2008.06.11} }`

- J. Lorenz, “Repeated Averaging and Bounded Confidence-Modeling, Analysis and Simulation of Continuous Opinion Dynamics,” , 2007. [Abstract]

This thesis is about dynamical systems of agents which perform repeated averaging under bounded confidence. It contributes to questions of their modeling, mathematical analysis and simulation. Modeling. The main modeling issue is continuous opinion dynamics. This includes dynamics of agents in a political opinion space as well as dynamics of collective motion in swarms of mobile autonomous robots. The existing bounded confidence models of Hegselmann-Krause and Deffuant-Weisbuch are presented under a common general framework. For both models density-based versions are introduced which give additional options for analysis. Surprising phenomena are presented, e.g. fostering consensus by lowering confidence or by introducing heterogeneity. Mathematics. Conditions for convergence to consensus are derived for systems where dynamics is driven by very generally defined averaging maps. If averaging maps are linear then the fundamental part of the system is a product of row-stochastic matrices infinite two the left. Several conditions, examples and counter-examples for convergence of such products are given. Thereby, we extend a result about convergence to consensus: Intercommunication intervals need not be bounded but may grow very very slow. Finally, the sets of fixed points are characterized for the bounded confidence models. Simulation. Interesting candidates for this class of systems are the models of opinion dynamics under bounded confidence, which are then analyzed via computer simulation. The density-based modeling approach is used to get a systematic overview for the case when agents’ initial opinions are uniformly distributed in the opinion space. Dynamics always converges to situations with clustered opinions. Bifurcation diagrams for attractive clustered states are computed for homogeneous bounds of confidence as well as extended phase diagrams for the consensus transitions in populations with two different levels of confidence. The thesis concludes with some advices on how to foster consensus in continuous opinion dynamics under bounded confidence and a list of open problems.

`@Other{Lorenz2007RepeatedAveragingand, Title = {{Repeated Averaging and Bounded Confidence-Modeling, Analysis and Simulation of Continuous Opinion Dynamics}}, Abstract = {This thesis is about dynamical systems of agents which perform repeated averaging under bounded confidence. It contributes to questions of their modeling, mathematical analysis and simulation. Modeling. The main modeling issue is continuous opinion dynamics. This includes dynamics of agents in a political opinion space as well as dynamics of collective motion in swarms of mobile autonomous robots. The existing bounded confidence models of Hegselmann-Krause and Deffuant-Weisbuch are presented under a common general framework. For both models density-based versions are introduced which give additional options for analysis. Surprising phenomena are presented, e.g. fostering consensus by lowering confidence or by introducing heterogeneity. Mathematics. Conditions for convergence to consensus are derived for systems where dynamics is driven by very generally defined averaging maps. If averaging maps are linear then the fundamental part of the system is a product of row-stochastic matrices infinite two the left. Several conditions, examples and counter-examples for convergence of such products are given. Thereby, we extend a result about convergence to consensus: Intercommunication intervals need not be bounded but may grow very very slow. Finally, the sets of fixed points are characterized for the bounded confidence models. Simulation. Interesting candidates for this class of systems are the models of opinion dynamics under bounded confidence, which are then analyzed via computer simulation. The density-based modeling approach is used to get a systematic overview for the case when agents' initial opinions are uniformly distributed in the opinion space. Dynamics always converges to situations with clustered opinions. Bifurcation diagrams for attractive clustered states are computed for homogeneous bounds of confidence as well as extended phase diagrams for the consensus transitions in populations with two different levels of confidence. The thesis concludes with some advices on how to foster consensus in continuous opinion dynamics under bounded confidence and a list of open problems.}, Author = {Lorenz, Jan}, Collaborationtags = {JL publicationlist}, Groups = {public}, Institution = {University of Bremen}, Intrahash = {9bec6105548d42cb7df60214fdabaa45}, Keywords = {emmyown}, Month = {March}, Owner = {janlo}, School = {Universit\"at Bremen}, Timestamp = {2008.04.16}, Type = {Doctoral Dissertation}, Url = {http://nbn-resolving.de/urn:nbn:de:gbv:46-diss000106688}, Year = {2007} }`

- J. Lorenz and D. Urbig, “About the power to enforce and prevent consensus by manipulating communication rules,” Advances in Complex Systems, vol. 10, iss. 2, p. 251â€“269, 2007. [Abstract]

We explore the possibilities of enforcing and preventing consensus in continuous opinion dynamics that result from modifications in the communication rules. We refer to the model of Weisbuch and Deffuant, where n agents adjust their continuous opinions as a result of random pairwise encounters whenever their opinions differ not more than a given bound of confidence epsilon. A high epsilon leads to consensus, while a lower epsilon leads to a fragmentation into several opinion clusters. We drop the random encounter assumption and ask: How small may epsilon be such that consensus is still possible with a certain communication plan for the entire group? Mathematical analysis shows that epsilon may be significantly smaller than in the random pairwise case. On the other hand we ask: How large may epsilon be such that preventing consensus is still possible? In answering this question we prove Fortunato’s simulation result that consensus cannot be prevented for epsilon>0.5 for large groups. Next we consider opinion dynamics under different individual strategies and examine their power to increase the chances of consensus. One result is that balancing agents increase chances of consensus, especially if the agents are cautious in adapting their opinions. However, curious agents increase chances of consensus only if those agents are not cautious in adapting their opinions.

`@Article{Lorenz.Urbig2007AboutPowerto, Title = {About the power to enforce and prevent consensus by manipulating communication rules}, Author = {Lorenz, Jan and Urbig, Diemo}, Journal = {Advances in Complex Systems}, Year = {2007}, Number = {2}, Pages = {251--269}, Volume = {10}, Abstract = {We explore the possibilities of enforcing and preventing consensus in continuous opinion dynamics that result from modifications in the communication rules. We refer to the model of Weisbuch and Deffuant, where n agents adjust their continuous opinions as a result of random pairwise encounters whenever their opinions differ not more than a given bound of confidence epsilon. A high epsilon leads to consensus, while a lower epsilon leads to a fragmentation into several opinion clusters. We drop the random encounter assumption and ask: How small may epsilon be such that consensus is still possible with a certain communication plan for the entire group? Mathematical analysis shows that epsilon may be significantly smaller than in the random pairwise case. On the other hand we ask: How large may epsilon be such that preventing consensus is still possible? In answering this question we prove Fortunato's simulation result that consensus cannot be prevented for epsilon>0.5 for large groups. Next we consider opinion dynamics under different individual strategies and examine their power to increase the chances of consensus. One result is that balancing agents increase chances of consensus, especially if the agents are cautious in adapting their opinions. However, curious agents increase chances of consensus only if those agents are not cautious in adapting their opinions.}, Collaborationtags = {JL publicationlist}, Doi = {10.1142/S0219525907000982}, Eprint = {0708.3244}, Eprinttype = {arxiv}, Groups = {public}, Intrahash = {3c5ef56b897f904aac08cf4cdddf0da9}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2008.04.16} }`

### 2006

- J. Lorenz, “Consensus strikes back in the Hegselmann-Krause model of continuous opinion dynamics under bounded confidence,” Journal of Artificial Societies and Social Simulation, vol. 9, iss. 1, p. 8, 2006. [Abstract]

The agent-based bounded confidence model of opinion dynamics of Hegselmann and Krause (2002) is reformulated as an interactive Markov chain. This abstracts from individual agents to a population model which gives a good view on the underlying attractive states of continuous opinion dynamics. We mutually analyse the agent-based model and the interactive Markov chain with a focus on the number of agents and onesided dynamics. Finally, we compute animated bifurcation diagrams that give an overview about the dynamical behavior. They show an interesting phenomenon when we lower the bound of confidence: After the first bifurcation from consensus to polarisation consensus strikes back for a while.

`@Article{Lorenz2006Consensusstrikesback, Title = {{Consensus strikes back in the Hegselmann-Krause model of continuous opinion dynamics under bounded confidence}}, Author = {Lorenz, Jan}, Journal = {Journal of Artificial Societies and Social Simulation}, Year = {2006}, Number = {1}, Pages = {8}, Volume = {9}, Abstract = {The agent-based bounded confidence model of opinion dynamics of Hegselmann and Krause (2002) is reformulated as an interactive Markov chain. This abstracts from individual agents to a population model which gives a good view on the underlying attractive states of continuous opinion dynamics. We mutually analyse the agent-based model and the interactive Markov chain with a focus on the number of agents and onesided dynamics. Finally, we compute animated bifurcation diagrams that give an overview about the dynamical behavior. They show an interesting phenomenon when we lower the bound of confidence: After the first bifurcation from consensus to polarisation consensus strikes back for a while.}, Collaborationtags = {JL publicationlist}, Groups = {public}, Intrahash = {0501a34f6e1e37d321f83a94a0e04d87}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2008.04.16}, Url = {http://jasss.soc.surrey.ac.uk/9/1/8.html} }`

- J. Lorenz, “Continuous Opinion Dynamics of Multidimensional Allocation Problems under Bounded Confidence. More dimensions lead to better chances for consensus,” European Journal of Economic and Social Systems, vol. 19, iss. 2, p. 213â€“227, 2006. [Abstract]

We study multidimensional continuous opinion dynamics, where opinions are nonnegative vectors which components sum up to one. Examples of such opinions are budgets or other allocation vectors which display a distribution of a fixed amount of ressource to n projects. We use the opinion dynamics models of Deffuant-Weisbuch and Hegselmann-Krause, which both extend naturally to more dimensional opinions. They both rely on bounded confidence of the agents and differ in their communication regime. We show detailed simulation results regarding n=2,…,8 and the bound of confidence epsilon. Number, location and size of opinion clusters in the stabilized opinion profiles are of interest. Known differences of both models repeat under higher opinion dimensions: Higher number of clusters and more minor clusters in the Deffuant-Weisbuch model, meta-stable states in the Hegselmann-Krause model. But surprisingly, higher dimensions lead to better chances for a vast majority consensus even for lower bounds of confidence. On the other hand, the number of minority clusters rises with n, too.

`@Article{Lorenz2006ContinuousOpinionDynamics, Title = {{Continuous Opinion Dynamics of Multidimensional Allocation Problems under Bounded Confidence. More dimensions lead to better chances for consensus}}, Author = {Jan Lorenz}, Journal = {European Journal of Economic and Social Systems}, Year = {2006}, Number = {2}, Pages = {213--227}, Volume = {19}, Abstract = {We study multidimensional continuous opinion dynamics, where opinions are nonnegative vectors which components sum up to one. Examples of such opinions are budgets or other allocation vectors which display a distribution of a fixed amount of ressource to n projects. We use the opinion dynamics models of Deffuant-Weisbuch and Hegselmann-Krause, which both extend naturally to more dimensional opinions. They both rely on bounded confidence of the agents and differ in their communication regime. We show detailed simulation results regarding n=2,...,8 and the bound of confidence epsilon. Number, location and size of opinion clusters in the stabilized opinion profiles are of interest. Known differences of both models repeat under higher opinion dimensions: Higher number of clusters and more minor clusters in the Deffuant-Weisbuch model, meta-stable states in the Hegselmann-Krause model. But surprisingly, higher dimensions lead to better chances for a vast majority consensus even for lower bounds of confidence. On the other hand, the number of minority clusters rises with n, too.}, Collaborationtags = {JL publicationlist}, Doi = {10.3166/ejess.19.213-227}, Eprint = {0708.2923}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26} }`

- J. Lorenz, “Convergence of Products of Stochastic Matrices with Positive Diagonals and the Opinion Dynamics Background,” in Lecture Notes in Computer Science: Positive Systems, Springer, 2006, vol. 341, p. 209â€“216. [Abstract]

We present a convergence result for infinite products of stochastic matrices with positive diagonals. We regard infinity of the product to the left. Such a product converges partly to a fixed matrix if the minimal positive entry of each matrix does not converge too fast to zero and if either zero-entries are symmetric in each matrix or the length of subproducts which reach the maximal achievable connectivity is bounded.

`@InCollection{Lorenz2006ConvergenceofProducts, Title = {{Convergence of Products of Stochastic Matrices with Positive Diagonals and the Opinion Dynamics Background}}, Author = {Lorenz, Jan}, Booktitle = {Lecture Notes in Computer Science: Positive Systems}, Publisher = {Springer}, Year = {2006}, Pages = {209--216}, Series = {Lecture Notes in Control and Information Sciences}, Volume = {341}, Abstract = {We present a convergence result for infinite products of stochastic matrices with positive diagonals. We regard infinity of the product to the left. Such a product converges partly to a fixed matrix if the minimal positive entry of each matrix does not converge too fast to zero and if either zero-entries are symmetric in each matrix or the length of subproducts which reach the maximal achievable connectivity is bounded.}, Collaborationtags = {JL publicationlist}, Doi = {10.1007/11757344}, Eprint = {0708.3177}, Journal = {Positive Systems}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26} }`

### 2005

- J. Lorenz, “Continuous opinion dynamics: Insights through interactive Markov chains,” in Proceedings of IASTED Conference Modelling, Simulation and Optimization MSO, Oranjestaad, Aruba, , 2005. [Abstract]

We reformulate the agent-based opinion dynamics models of Weisbuch-Deffuant and Hegselmann-Krause as interactive Markov chains. So we switch the scope from a finite number of n agents to a finite number of n opinion classes. Thus, we will look at an infinite population distributed to opinion classes instead of agents with real number opinions. The interactive Markov chains show similar dynamical behavior as the agent-based models: stabilization and clustering. Our framework leads to a discrete bifurcation diagram for each model which gives a good view on the driving forces and the attractive states of the system. The analysis shows that the emergence of minor clusters in the Weisbuch-Deffuant model and of meta-stable states with very slow convergence to consensus in the Hegselmann Krause model are intrinsic to the dynamical behavior.

`@InCollection{Lorenz2005Continuousopiniondynamics, Title = {Continuous opinion dynamics: Insights through interactive Markov chains}, Author = {Jan Lorenz}, Booktitle = {Proceedings of IASTED Conference Modelling, Simulation and Optimization MSO, Oranjestaad, Aruba}, Year = {2005}, Abstract = {We reformulate the agent-based opinion dynamics models of Weisbuch-Deffuant and Hegselmann-Krause as interactive Markov chains. So we switch the scope from a finite number of n agents to a finite number of n opinion classes. Thus, we will look at an infinite population distributed to opinion classes instead of agents with real number opinions. The interactive Markov chains show similar dynamical behavior as the agent-based models: stabilization and clustering. Our framework leads to a discrete bifurcation diagram for each model which gives a good view on the driving forces and the attractive states of the system. The analysis shows that the emergence of minor clusters in the Weisbuch-Deffuant model and of meta-stable states with very slow convergence to consensus in the Hegselmann Krause model are intrinsic to the dynamical behavior.}, Collaborationtags = {JL publicationlist}, Eprint = {0708.3293}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {https://arxiv.org/pdf/0708.3293.pdf} }`

- J. Lorenz, “A stabilization theorem for dynamics of continuous opinions,” Physica A: Statistical Mechanics and its Applications, vol. 355, iss. 1, p. 217â€“223, 2005. [Abstract]

A stabilization theorem for processes of opinion dynamics is presented. The theorem is applicable to a wide class of models of continuous opinion dynamics based on averaging (like the models of Hegselmann-Krause and Weisbuch-Deffuant). The analysis detects self-confidence as a driving force of stabilization.

`@Article{Lorenz2005stabilizationtheoremdynamics, Title = {{A stabilization theorem for dynamics of continuous opinions}}, Author = {Lorenz, Jan}, Journal = {Physica A: Statistical Mechanics and its Applications}, Year = {2005}, Number = {1}, Pages = {217--223}, Volume = {355}, Abstract = {A stabilization theorem for processes of opinion dynamics is presented. The theorem is applicable to a wide class of models of continuous opinion dynamics based on averaging (like the models of Hegselmann-Krause and Weisbuch-Deffuant). The analysis detects self-confidence as a driving force of stabilization.}, Collaborationtags = {JL publicationlist}, Doi = {10.1016/j.physa.2005.02.086}, Eprint = {0708.2981}, Eprinttype = {arxiv}, Groups = {public}, Intrahash = {4499310eb6b6c4173bb2852586ed5fb8}, Jlprojects = {bounded_confidence,continuous_opinion_dynamics, EMMY_clusters}, Keywords = {emmyown}, Owner = {janlo}, Publisher = {Elsevier}, Timestamp = {2008.04.16} }`

- J. Lorenz and F. Menino, “Designing Participatory Budgeting: Mathematics of opinion dynamics and aggregation,” Policy papers of the Friedrich Ebert Foundation’s Brazil Projekt 2005 2005. [Abstract]

In this paper, participatory budgeting (especially in Porto Alegre) is seen as a formal mathematical model of opinion dynamics and social-choice. We relate it to the classical social-choice problem mixed with the ideas of the wisdom of crowds and social consensus. Mathematical Simulation of opinion dynamics are done to analyse the impact of the mechanism on the finding of a social consensus. With these mathematical results, we aim to contribute to the debate about introducing participatory budgeting in Germany and how to extend it in Brazil. The simulation of opinion dynamics leads to the result that giving the crowd more categories to distribute money to has an impact that fosters at least a vast majority of the group to find a social consensus. But this effect slows down so that about eight categories seem to be enough. We conclude by putting our results into the general picture covering the political barriers of implementing participatory budgeting: representation, power balance and institutionalization. And we add some remarks about participatory budgeting as a tool to evolve from technobureaucracy to technodemocracy and about the civil society as a forth force in democracy.

`@TechReport{Lorenz.Menino2005DesigningParticipatoryBudgeting, Title = {{Designing Participatory Budgeting: Mathematics of opinion dynamics and aggregation}}, Author = {Jan Lorenz and Frederico Menino}, Institution = {Policy papers of the Friedrich Ebert Foundation's Brazil Projekt 2005}, Year = {2005}, Abstract = {In this paper, participatory budgeting (especially in Porto Alegre) is seen as a formal mathematical model of opinion dynamics and social-choice. We relate it to the classical social-choice problem mixed with the ideas of the wisdom of crowds and social consensus. Mathematical Simulation of opinion dynamics are done to analyse the impact of the mechanism on the finding of a social consensus. With these mathematical results, we aim to contribute to the debate about introducing participatory budgeting in Germany and how to extend it in Brazil. The simulation of opinion dynamics leads to the result that giving the crowd more categories to distribute money to has an impact that fosters at least a vast majority of the group to find a social consensus. But this effect slows down so that about eight categories seem to be enough. We conclude by putting our results into the general picture covering the political barriers of implementing participatory budgeting: representation, power balance and institutionalization. And we add some remarks about participatory budgeting as a tool to evolve from technobureaucracy to technodemocracy and about the civil society as a forth force in democracy.}, Collaborationtags = {JL publicationlist}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {http://www.janlo.de/papers/lorenz_menino_brazil_2005.pdf} }`

### 2004

- H. Ramke, S. Mues, and J. Lorenz, “Vom Resteverkauf zum Verwertungsmanagement,” Der Versandhausberater, 2004.
`@Article{Ramke.Mues.ea2004VomResteverkaufzum, Title = {{Vom Resteverkauf zum Verwertungsmanagement}}, Author = {Hubert Ramke and Stefan Mues and Jan Lorenz}, Journal = {Der Versandhausberater}, Year = {2004}, Month = {April}, Collaborationtags = {JL publicationlist}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.29} }`

### 2003

- J. Lorenz, “Mehrdimensionale Meinungsdynamik bei wechselndem Vertrauen,” , 2003.
`@Other{Lorenz2003MehrdimensionaleMeinungsdynamikbei, Title = {{Mehrdimensionale Meinungsdynamik bei wechselndem Vertrauen}}, Author = {Jan Lorenz}, Collaborationtags = {JL publicationlist}, Groups = {public}, Institution = {University of Bremen}, Intrahash = {92937a1285300bb796e2a0e91ff4eb00}, Keywords = {emmyown}, Month = {Feb.}, Owner = {janlo}, School = {Universit\"at Bremen}, Timestamp = {2009.06.26}, Type = {Diploma Thesis}, Url = {http://elib.suub.uni-bremen.de/dipl/docs/00000056.pdf}, Year = {2003} }`

- J. Lorenz, “Multidimensional Opinion Dynamics when Confidence Changes,” in Economic Complexity 2003 Aix-en-Provence, France, May 8-12, 2003. [Abstract]

A group of m agents is to find a common agreement about a certain issue. Consider this issue to be an n-dimensional vector of real numbers, for example the allocation of a fixed sum of money to n projects. Each agent has an opinion about the allocation, which he may revise. We model the process of opinion formation as a time-discrete dynamical system, in which every agent averages all opinions which are closed to his own opinion. Thus confidence structures may change. Mathematical analysis shows that every starting opinion distribution converges to a stable distribution. The driving force of this convergence is self-confidence. Further, we present some simulation results concerning m equal 150 and n equal 3, which give an insight into the dynamics of multidimensional opinion dynamics under bounded confidence.

`@InProceedings{Lorenz2003MultidimensionalOpinionDynamics, Title = {{Multidimensional Opinion Dynamics when Confidence Changes}}, Author = {Jan Lorenz}, Booktitle = {Economic Complexity 2003 Aix-en-Provence, France, May 8-12}, Year = {2003}, Abstract = {A group of m agents is to find a common agreement about a certain issue. Consider this issue to be an n-dimensional vector of real numbers, for example the allocation of a fixed sum of money to n projects. Each agent has an opinion about the allocation, which he may revise. We model the process of opinion formation as a time-discrete dynamical system, in which every agent averages all opinions which are closed to his own opinion. Thus confidence structures may change. Mathematical analysis shows that every starting opinion distribution converges to a stable distribution. The driving force of this convergence is self-confidence. Further, we present some simulation results concerning m equal 150 and n equal 3, which give an insight into the dynamics of multidimensional opinion dynamics under bounded confidence.}, Collaborationtags = {JL publicationlist}, Groups = {public}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {http://www.janlo.de/papers/lorenz_aix_2003.pdf} }`

- J. Lorenz, “Opinion Dynamics with Heterogeneous Bounds of Confidence for the Agents,” in Conference ”WEHIA 2003”, Institute for World Economics, Kiel, Germany, May 29-31, 2003. [Abstract]

A group of m agents is to find a common agreement about a certain issue. An opinion is represented by a real number. We assume that each agent changes his opinion by repeated averaging over the opinions of his agents of confidence. An agent i should be an agent of confidence for agent j, if the opinion of agent i lies within the confidence interval of agent j, which is an interval with the opinion of j in the center. This iterated process is called opinion formation under bounded confidence. In this paper we model heterogenity of agents by admitting differences in the sizes of the confidence intervals for each agent. We examine what happens if some of the agents have higher/lower bounds of confidence than the other agents. We focus on how the possibility of reaching a consensus among all agents changes through heterogenity. Interestingly there are both, positive and negative effects. Simulations show that the positive effects might be bigger. In addition, we present some mathematical results about the convergence of this kind of opinion formation.

`@InProceedings{Lorenz2003OpinionDynamicswith, Title = {{Opinion Dynamics with Heterogeneous Bounds of Confidence for the Agents}}, Author = {Jan Lorenz}, Booktitle = {Conference ''WEHIA 2003'', Institute for World Economics, Kiel, Germany, May 29-31}, Year = {2003}, Abstract = {A group of m agents is to find a common agreement about a certain issue. An opinion is represented by a real number. We assume that each agent changes his opinion by repeated averaging over the opinions of his agents of confidence. An agent i should be an agent of confidence for agent j, if the opinion of agent i lies within the confidence interval of agent j, which is an interval with the opinion of j in the center. This iterated process is called opinion formation under bounded confidence. In this paper we model heterogenity of agents by admitting differences in the sizes of the confidence intervals for each agent. We examine what happens if some of the agents have higher/lower bounds of confidence than the other agents. We focus on how the possibility of reaching a consensus among all agents changes through heterogenity. Interestingly there are both, positive and negative effects. Simulations show that the positive effects might be bigger. In addition, we present some mathematical results about the convergence of this kind of opinion formation.}, Collaborationtags = {JL publicationlist}, Keywords = {emmyown}, Owner = {janlo}, Timestamp = {2009.06.26}, Url = {http://www.janlo.de/papers/lorenz_kiel_2003.pdf} }`