The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis
Journal of Artificial Societies and Social Simulation
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Received: 04-Jun-2009 Accepted: 26-Sep-2009 Published: 31-Oct-2009
|Table 1: Data Set|
|Number of citations||2873||5375||8248|
|Number of JASSS articles|
Avg. citations per article
|Frequency of occurrence of citations in JASSS (xk)||fk||pk||cpk||fk||pk||cpk|
|Number of different citation sources||2363||4311|
|Avg. source age||10.85||10.91|
|Table 2. Citation Values of the Most Cited Sources for the Two Time Periods|
|Figure 1. Publication Sources over 10 Years of JASSS|
|Figure 2. Publication Sources over the Two Time Periods|
|Figure 3. Most Frequently Cited Journals in JASSS between 1997 and 2008|
|Figure 4. Disciplines of Journal Citations between 1997 and 2008|
|Figure 5. Co-Citation Network 1998-2002|
|Figure 6. Co-Citation Network 2003-2007|
2 As Mullins et al. (1977) and McCain (1986) show, the co-citation structure is relatively reliable in comparison with the results of a survey for reflecting how involved the researchers are perceived to be. Because these networks are not necessarily formally linked, they are often referred to as "invisible colleges". See Crane (1972) and Lievrouw (1989).
3 Following Gmür (2003), we define a threshold of 0.25 in order to focus on the strongest links.
4 See http://jasss.soc.surrey.ac.uk. Generating the data directly also had the advantage of avoiding the typical problems associated with using the data from SSCI, such as a high percentage of errors. See Moed (2002).
5 Similarity of two citations was defined for the HTML Parser as a match of at least to 90% between the first corresponding authors and the words in the cited text.
6 Abbreviations: fk = absolute frequency, pk = relative frequency, cpk = cumulative relative frequency.
7 See Gilbert (1997). As mentioned above, in our study we analyse all citations with a frequency of three or more, which represent about five per cent of the whole data set.
8 Such a tendency can have several causes, like changes in technology resulting in an easier on-line access to journals.
9 See e.g. Ramos-Rodriguez and Ruiz-Navarro (2004) or Meyer et al. (2008).
10 See e.g. Ramos-Rodriguez and Ruiz-Navarro (2004) or Meyer et al. (2008).
11 The ISI journal categorization has been constructed based on journal subject content and citation information. See Klavans and Boyack (2006: 253). For a similar approach to assess the interdisciplinary breadth of a field see Ponzi (2002).
12 To provide some more structure we subsumed related ISI subject categories in Figure 4 additionally under more general categories such as economics & management, social science and biological sciences.
14 Given g nodes in a cluster, there are g (g-1) / 2 possible relationships. With L as the number of actual relationships, density is defined as 2L / g(g-1). See Iacobucci (1994:101-103). The density for the different clusters is given in the appendix.
15 To support our decision in favor of certain designations as far as possible, we discussed them with a number of experts. Moreover, the results were presented to several international seminar and conference audiences for additional feedback.
16 The remaining 43 publications do not exhibit any or fewer than three co-citation relationships to other sources and are therefore not included.
17 A list of publications included in the networks is provided in the appendix. This list is structured along the different clusters and groups and can be accessed directly via the respective hyperlinks in the text.
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|Table A1: Publications of Co-citation Network 1 (1998-2002)|
|Group||Publication||No. of links (within the group)|
|1.1 Norms (description) (picture)|
D = 0.56
|WALKER, A and WOOLDRIDGE, M J (1995). Understanding the emergence of conventions in multi-agent systems. Proceedings of the First International Conference on Multiagent Systems (ICMAS'95), San Francisco, CA: AAAI Press, pp. 384-389.||8|
|SHOHAM, Y and TENNEHOLTZ, M (1992). On the synthesis of useful social laws for artificial agent societies (preliminary report). Proceedings of the AAAI Conference, pp. 276-281.||6|
|SHOHAM, Y and TENNEHOLTZ, M (1992). Emergent conventions in multi agent systems: Initial experimental results and observations. Proceedings of the 3rd International Conference on KR&R, pp. 225-232.||5|
|CONTE, R and CASTELFRANCHI, C (1995). Understanding the Functioning of Norms in Social Groups through Simulation. In Gilbert G N and Conte R (Ed.): Artificial Societies: The Computer Simulation of Social Life. London: UCL Press, pp. 252-267||4|
|CASTELFRANCHI, C, CONTE, R and PAOLUCCI, M (1998). Normative Reputation and the Costs of Compliance. Journal of Artificial Societies and Social Simulation, vol. 1, no. 3.||4|
|SAAM, N J and HARRER, A (1999). Simulating Norms, Social Inequality, and Functional Change in Artificial Societies. Journal of Artificial Societies and Social Simulation, vol. 2, no. 1.||4|
|TRIVERS, R L (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, Vol. 46. pp. 35-57.||4|
|SCHELLING, T C (1960). The strategy of conflict. Oxford: Oxford University Press.||3|
|HOMANS, G (1950). The human group. New York: Harcourt Brace.||2|
|1.2 Learning and Genetic Algorithms (description) (picture) |
D = 0.36
|KOZA, J (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.||4|
|MOSS, S and EDMONDS, B (1998). Modelling economic learning as modelling. Cybernetics and Systems, Vol. 29, pp. 215-247.||3|
|HOLLAND, J H et al. (1986). Induction: processes of inference, learning and discovery. Cambridge, MA: MIT Press.||3|
|ARTHUR, B W (1994). Inductive reasoning and bounded rationality. American Economic Association Papers and Proceedings, 84, 406-411.||3|
|CONTE, R and CASTELFRANCHI, C (1995). Cognitive and Social Action. London: UCL Press.||2|
|HOLLAND, J H (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.||2|
|GOLDBERG, D E (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.||2|
|OLSON, M (1965). The Logic of Collective Action. Cambridge, MA: Harvard University Press.||1|
|1.3a Paradigmatic Issues of Economics and Social Simulation (description) (picture) |
D = 0.27
|GILBERT, N and CONTE R (1995). Artificial Societies: the computer simulation of social life. London: UCL Press.||5|
|HODGSON, G M and GEOFFREY M (1988). Economics and Institutions: A Manifesto for a Modern Institutional Economics. Cambridge, MA: Polity Press.||4|
|ORMEROD, P (1995). The Death of Economics. London: Faber and Faber.||3|
|ORMEROD, P (1998). Butterfly Economics. London: Faber and Faber.||3|
|ARTHUR, W B, DURLAUF S N and LANE D A (1997). The Economy as an Evolving Complex System II. Reading, MA: Addison-Wesley.||3|
|GILBERT N (1995). Emergence in social simulation. In Gilbert G N and Conte R (Ed.): Artificial Societies: The Computer Simulation of Social Life. London: UCL Press, pp. 144-156.||2|
|MACY, M W (1998). Social Order in Artificial Worlds. Journal of Artificial Societies and Social Simulation, Vol. 1, No. 1.||1|
|ARTHUR, WB (1994). Increasing Returns and Path Dependence in the Economy. Ann Arbor, MI: University of Michigan Press.||1|
|GOLDSPINK, C. (2000). Modelling Social Systems as Complex: Towards a Social Simulation Meta-model. Journal of Artificial Societies and Social Simulation, Vol. 3, No.2.||1|
|WOOLDRIDGE, M and JENNINGS, N (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, Vol. 10, No. 2, pp. 115-152.||1|
|1.3b Methodological Aspects of Social Simulation (description) (picture) |
D = 0.38
|GILBERT, G N and TROITZSCH, K G (1999). Simulation for the Social Scientist. Buckingham: Open University Press.||4|
|TROITZSCH, K G (1997). Social science simulation - Origins, prospects, purposes. In Conte, R et al. (Eds) Simulating social phenomena. Heidelberg: Springer, pp. 40-54.||4|
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|AXELROD, R (1997). Advancing the Art of Simulation in the Social Sciences. In Conte R, Hegselmann R and Terna P (Ed.): Simulating Social Phenomena. Berlin: Springer||1|
|1.4 Psychology / Economics and Technology (description) (picture) |
D = 0.25
|DAVID, P A (1985). Clio and the Economics of QWERTY. American Economic Review, Vol. 75, pp. 332-337.||4|
|FESTINGER, L (1954). A theory of social comparison processes. Human Relations, Vol. 7, pp. 117-140.||3|
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|BANDURA, A (1977). Social Learning Theory. Englewood Cliffs, New Jersey: Prentice Hall.||1|
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|2 Evolutionary Perspective (description) (picture) |
D = 0.50
|BOYD, R and Richerson, P (1985). Culture and the evolutionary process. Chicago: Chicago University Press.||3|
|HOLLAND, J H (1998). Emergence: from chaos to order. Reading, MA: Addison Wesley.||3|
|DENNETT, D (1995). Darwin's Dangerous Idea. New York: Simon & Schuster.||2|
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|Table A2: Publications of Co-citation Network 2 (2003-2007)|
|Group||Publication||No. of links (within the group)|
|1.1 Networks and Diffusion (description) (picture) |
D = 0.43
|KUPERMAN, M and ABRAMSON, G (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, Vol. 86, No. 13, pp. 2909-2912.||4|
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|WATTS, D J and DUNCAN, J (1999). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton, NJ: Princeton University Press.||2|
|COWAN, R and JONARD, N (2004). Network structure and the diffusion of knowledge. Annual Review of Psychology, Vol. 55, pp. 591-621.||2|
|ALBERT, R and BARABSI, A L (2002). Statistical Mechanics of Complex Networks. Review of Modern Physics, Vol. 74, pp. 47-97.||2|
|HUANG C Y, SUN C T, HSIEH J L, and LIN H (2004). Simulating SARS: Small-World Epidemiological Modelling and Public Health Policy Assessments. Journal of Artificial Societies and Social Simulation, 2004, Vol. 7, No. 4.||1|
|1.2a Modeling Pitfalls (description) (picture) |
D = 0.56
|POLHILL, J G, IZQUIERDO, L R and GOTTS, N M (2005). What every agent based modeller should know about floating point arithmetic. Environmental Modelling and Software. Vol. 21, pp. 283-309.||9|
|POLHILL J G, IZQUIERDO, L R and GOTTS, N M (2005). The ghost in the model (and other effects of floating point arithmetic). Journal of Artificial Societies and Social Simulation, Vol. 8, No. 1.||8|
|LEBARON, B, ARTHUR, W B and PALMER, R (1999). Time series properties of an artificial stock market. Journal of Economic Dynamics and Control, Vol. 23, pp. 1487-1516.||7|
|GOTTS, N M, POLHILL, J G and LAW, A N R (2003). Aspiration levels in a land-use simulation. Cybernetics and Systems, Vol. 34, pp. 663-683.||6|
|POLHILL, J G, GOTTS, N M and LAW, A N R (2001). Imitative versus nonimitative strategies in a land use simulation. Cybernetics and Systems, Vol. 32, pp. 85-307.||6|
|HIGHAM, N J (2002). Accuracy and Stability of Numerical Algorithm. 2nd ed. Philadelphia: Society for Industrial and Applied Mathematics.||5|
|IEEE (1985). IEEE Standard for Binary Floating-Point Arithmetic. New York, NY: Institute of Electrical and Electronics Engineers.||5|
|JOHNSON, P E (2002). Agent-Based Modeling: What I learned from the Artificial Stock Market. Social Science Computer Review, Vol. 20, pp. 174-186.||5|
|KNUTH, D E (1998). The Art of Computer Programming. Third Edition. Boston, MA: Addison-Wesley.||5|
|AXELROD, R (1986). An Evolutionary approach to norms. American Political Science Review, Vol. 80, No. 4, pp. 1095-1111.||3|
|GALAN J M and IZQUIERDO L R (2005). Appearances can be deceiving: Lessons learned re-implementing Axelrod's 'Evolutionary Approach to Norms'. Journal of Artificial Societies and Social Simulation, Vol. 8, No. 3.||3|
|1.2b Model Alignment (description) (picture) |
D = 0.40
|EDMONDS B and HALES D (2003). Replication, replication and replication: Some hard lessons from model alignment. Journal of Artificial Societies and Social Simulation, Vol. 6, No. 4.||2|
|AXTELL R, AXELROD R, EPSTEIN J and COHEN, M D (1996). Aligning Simulation Models: A Case Study and Results. Computational and Mathematical Organization Theory, Vol. 1, No. 1, pp. 123-141.||2|
|HALES D, ROUCHIER J and EDMONDS B (2003). Model-to-Model Analysis. Journal of Artificial Societies and Social Simulation, Vol. 6, No. 4.||2|
|CARLEY K M, PRIETULA M J and LIN Z (1998). Design Versus Cognition: The interaction of agent cognition and organizational design on organizational performance. Journal of Artificial Societies and Social Simulation, Vol. 1, No. 3||2|
|CARLEY K M (1992). Organizational Learning and Personnel Turnover. Organization Science, Vol. 3, No. 1, pp. 20-46.||1|
|1.2c (undescribed branch) (picture)||EPSTEIN J M and Axtell R L (1996) Growing Artificial Societies: Social Science from the Bottom Up. Washington D.C.: The Brookings Institution Press and Cambridge, Mass.: The MIT Press.||3|
|CONTE R, EDMONDS B, SCOTT M, SAWYER R K (2001). Sociology and Social Theory in Agent-Based Social Simulation: A Symposium. Computational and Mathematical Organization Theory, Vol. 7, pp. 183-205.||2|
|PARKER, M (2000). Ascape: Abstracting Complexity. Swarmfest Proceedings.||1|
|ZEIGLER B P (1976). Theory of Modelling and Simulation. New York: John Wiley.||1|
|CONTE R and CASTELFRANCHI C (1995). Cognitive and Social Action. London: UCL Press.||1|
|1.3a Diffusion and Social Networks (description) (picture) |
D = 0.67
|GRANOVETTER, M (1985). Economic Action and Social Structure: The problem of embeddedness. American Journal of Sociology, Vol. 91, No. 3, pp. 481-510.||6|
|YOUNG, P (1999). Diffusion in Social Networks. Working Paper No. 2, Brookings Institution.||5|
|VALENTE, T W (1995). Network Models of the Diffusion of Innovations. Cresskill, NJ: Hampton Press.||5|
|YOUNG, P (1998). Individual Strategy and social structure. Princeton, NJ: Princeton University Press.||4|
|WEIDLICH, W (2000). SocioDynamics: A Systematic Approach to Mathematical Modelling. Amsterdam: Harwood Academic Publishers.||4|
|EDWARDS M, HUET S, GOREAUD F and DEFFUANT G (2003). Comparing an individual-based model of behaviour diffusion with its mean field aggregate approximation. Journal of Artificial Societies and Social Simulation, Vol. 6, No. 4.||3|
|MORRIS, S (2000). Contagion. Review of Economic Studies, Vol. 67, No.1, pp. 57-78.||2|
|1.3b Cognitive Agents (description) (picture) |
D = 0.31
|NEWELL, A (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.||5|
|MOSS, S (1995). Control Metaphors in the Modelling of Economic Learning and Decision-Making Behavior. Computational Economics, Vol. 8, pp. 283-301.||4|
|MOSS, S (1998). Critical Incident Management: An Empirically Derived Computational Model. Journal of Artificial Societies and Social Simulation, Vol. 1, No. 4.||4|
|MOSS, S, GAYLARD, H. WALLIS, S and EDMONDS, B (1998). SDML: A Multi-Agent Language for Organizational Modelling. Computational Mathematical Organization Theory, Vol. 4, No. 1, pp. 43-69.||3|
|COHEN, P R (1985). Heuristic Reasoning: An Artificial Intelligence Approach. Pitman Advanced Publishing Program, Boston||3|
|CYERT, R and MARCH, J G (1992). A Behavioral Theory of the Firm. NJ: Prentice Hall.||3|
|EDMONDS, B and MOSS, S (2005). From KISS to KIDS an 'anti-simplistic' modelling approach. In Davidsson P, Logan B, Takadama K (Eds.) Multi Agent Based Simulation 2004. Lecture Notes in Artificial Intelligence. Springer, 3415, pp.130-144.||2|
|MARCH, J and SIMON, H (1958). Organizations. New York: Wiley.||2|
|MOSS, S and EDMONDS, B (2003). Sociology and Simulation: Statistical and Qualitative Cross-Validation. American Journal of Sociology, Vol. 110, No. 4, pp. 1095-1131.||1|
|COHEN, M D, March, J G and Olsen, J P (1972). A Garbage Can Model of Organizational Choice. Administrative Sciences Quarterly, Vol. 17, No. 1, pp. 1-25.||1|
|1.3c (undescribed branch) (picture)||GILBERT, G N and TROITZSCH, K G (1999). Simulation for the Social Scientist. Milton Keynes: Open University Press.||3|
|AXELROD, R (1997). The Complexity of Cooperation: Agent-based models of conflict and cooperation. Princeton, NJ: The Princeton University Press.||2|
|EPSTEIN, J M (1999). Agent-based computational models and generative social science. Complexity, Vol. 4, No. 5, pp. 41-60.||1|
|2.1 Reputation (description) (picture) |
D = 0.42
|KREPS, D M and WILSON, R (1982). Reputation and Imperfect Information. Journal of Economic Theory, Vol. 27, pp. 253-279.||6|
|CONTE, R and PAOLUCCI, M (2002). Reputation in artificial societies: Social beliefs for social order. Dordrecht: Kluwer Academic Publishers.||5|
|BOLTON, G E, KATOK, E and OCKENFELS, A (2004). How effective are electronic reputation mechanisms? An experimental investigation. Management Science, Vol. 50, No. 11, pp. 1587-1602.||4|
|DUNBAR, R (1998). Grooming, Gossip, and the Evolution of Language. Cambridge, MA: Harvard University Press.||4|
|RESNICK, P and ZECKHAUSER, R (2002). Trust among strangers in internet transactions: Empirical analysis of ebay's reputation system. In Baye, M R, ed. The Economics of the Internet and E-Commerce, Vol. 11 of Advances in Applied Microeconomics. Amsterdam: Elsevier Science.||4|
|SABATER, J and SIERRA, C (2002). Reputation and Social Network Analysis in Multi-Agent Systems. Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 475-82.||3|
|RAUB, W and WEESIE, J (1990). Reputation and efficiency in social interaction: an example of network effects. American Journal of Sociology, Vol. 96, No. 3, pp. 626-54.||2|
|FLACHE, A and HEGSELMANN, R (1999). Rationality vs. learning in the evolution of solidarity networks: A theoretical comparison. Computational and Mathematical Organization Theory, Vol. 5, No. 2, pp. 97-127.||1|
|SAWYER, R K (2003). Artificial societies: Multi agent systems and the micro-macro link in sociological theory. Sociological Methods and Research, Vol. 31, No. 3, pp. 325-363.||1|
|2.2 Reciprocity (description) (picture) |
D = 0.46
|BOYD, R and RICHERSON, P (1985). Culture and the evolutionary process. Chicago: Chicago University Press.||5|
|CAVALLI-SFORZA, L L and FELDMAN, M W (1981). Cultural transmission and evolution: a quantitative approach. Princeton: Princeton University Press.||4|
|HAMILTON, W D (1964). The genetic evolution of social behaviour. Journal of Theoretical Biology. Vol. 7, 1-16, 17-52.||4|
|DAWKINS, R (1976). The Selfish Gene. Oxford: Oxford University Press.||4|
|NOWAK, M A and SIGMUND, K (1998). Evolution of indirect reciprocity by image scoring. Nature, Vol. 393, pp. 573-577.||3|
|TRIVERS, R L (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, Vol. 46, pp. 35-57.||2|
|FEHR, E and FISCHBAUER, U (2003). The nature of human altruism. Nature, Vol. 425, pp.785-791.||2|
|HUTCHINS, E and HAZLEHURST, B (1995). How to invent a lexicon: the development of shared symbols in interaction. In Gilbert, G N and Conte, R (Ed.): Artificial Societies: The computer simulation of social life. London: UCL Press, pp. 157-189.||2|
|3 Opinion Dynamics (description) (picture) |
D = 0.32
|HEGSELMANN, R and KRAUSE, U (2002). Opinion Dynamics and Bounded Condence Models, Analysis and Simulation. Journal of Artificial Societies and Social Simulation, Vol. 5, No 3.||11|
|DEFFUANT G, NEAU D, AMBLARD F and WEISBUCH G (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, Vol. 3, pp. 87-98.||9|
|DITTMER, J (2001). Consensus formation under bounded confidence. Nonlinear Analysis, Vol. 47, pp. 4615-4621.||6|
|KRAUSE, U (1997). Soziale Dynamiken mit vielen Interakteuren. Eine Problemskizze. In Krause, U and Stöckler, M (Ed.): Modellierung und Simulation von Dynamiken mit vielen interagierenden Akteuren, Bremen: Bremen University, pp. 37-51.||6|
|DEFFUANT, G, AMBLARD, F, WEISBUCH, G and FAURE, T (2002). How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation, Vol. 5, No. 4.||6|
|AMBLARD, F and DEFFUANT, G (2004). The role of network topology on extremism propagation with the relative agreement opinion dynamics. Physica A, Vol. 343, pp. 725-738.||6|
|KRAUSE, U (2000). A Discrete Non-linear and Non-autonomous Model of Consensus Formation. In Elaydi S, Ladas G, Popenda J and Rakowski J (Eds.): Communications in Difference Equations, Amsterdam: Gordon and Breach Publ., pp. 227-236.||4|
|HEGSELMANN, R and KRAUSE, U (2005). Opinion Dynamics Driven by Various Ways of Averaging. Computational Economics, Vol. 25, pp. 381-405.||4|
|URBIG, D and LORENZ, J (2004). Communication regimes in opinion dynamics: Changing the number of communicating agents. Proceedings of the Second Conference of the European Social Simulation Association (ESSA).||4|
|WEISBUCH, G, DEFFUANT, G, and AMBLARD, F (2005). Persuasion dynamics. Physica A, Vol. 353, pp. 555-575.||4|
|BEN-NAIM, E, KRAPIVSKY, P L and REDNER, S (2003). Bifurcations and patterns in compromise processes. Physica D, Vol. 183, pp. 190-204.||3|
|DEFFUANT, G, AMBLARD, F and WEISBUCH, G (2004). Modelling Group Opinion Shift to Extreme: the Smooth Bounded Confidence Model. Presented to 2nd ESSA Conference, Valladolid, September 2004.||2|
|SZNAJD-WERON, K and SZNAJD, J (2000). Opinion Evolution in Closed Communities. International Journal of Modern Physics C, Vol.11, pp. 1157-1165.||1|
|WEISBUCH G, DEFFUANT G, AMBLARD F and NADAL J P (2001). Interacting agents and continuous opinion dynamics. Lecture Notes in Economics and Mathematical Systems, Vol. 521, pp. 225-242, 2002||1|
|WEISBUCH, G (2004). Bounded confidence and social networks. In European Physical Journal B, Special Issue: Application of Complex Networks in Biological Information and Physical Systems, Vol. 38, pp.339-343.||1|
|4 Environmental Aspects and Resource Use (description) (picture) |
D = 0.19
|VINCK, D (1999). Les objets intermédiaires dans les réseaux de coopération scientifique. Revue Française de Sociologie, Vol. 40, pp. 385-414.||5|
|D'AQUINO, P, LE PAGE, C, BOUSQUET, F and BAH, A (2003). Using Self-Designed Role-Playing Games and a Multi-Agent System to Empower a Local Decision-Making Process for Land Use Management: The SelfCormas Experiment in Senegal. Journal of Artificial Societies and Social Simulation, Vol. 6, No.3.||3|
|BARRETEAU, O, BOUSQUET, F and ATTONATY, J M (2001). Role-playing games for opening the black box of multi-agent systems: method and lessons of its application to Senegal River Valley irrigated systems. Journal of Artificial Societies and Social Simulation, Vol. 4, No. 2.||3|
|ETIENNE, M, LE PAGE, C and COHEN, M (2003). A step by step approach to build up land management scenarios based on multiple viewpoints on multi-agent systems simulations. Journal of Artificial Societies and Social Simulations, Vol. 6, No. 2.||3|
|BOUSQUET, F, BAKAM, I, PROTON, H and LE PAGE, C (1998). Cormas: Common-Pool Resources and Multi-Agent Systems. Lecture Notes in Artificial Intelligence, Vol. 1416, pp. 826-83.||2|
|D'AQUINO, P, BARRETEAU, O, ETIENNE, M, BOISSAU, S, AUBERT, S, BOUSQUET, F, LE PAGE, C and DARE, W (2002). The role-playing games in an AB participatory modelling process: outcomes from 5 experiments carried out in the last five years. Proceedings International Environmental Modelling and Software Society Conference, Lugano, Switzerland, Vol. 2, pp. 275-280.||2|
|BOUSQUET, F, BARRETEAU, O, LE PAGE, C, MULLON, C and WEBER, J (1999). An environmental modelling approach. The use of multi-agent simulations. In Blasco, F and Weill, A (Ed.): Advances in environmental and ecological modelling. Elsevier, pp.113-122.||2|
|BOUSQUET, F, BARRETEAU, O, D'AQUINO, P, ETIENNE, M, BOISSAU, S, AUBERT, S, LE PAGE, C, BABIN, D and CASTELLA, J-C (2002). Multi-agent systems and role games: an approach for ecosystem co-management. In Janssen M (Ed.): Complexity and ecosystem management: the theory and practice of multi-agent approaches. Northampton, England: Elgar Publishers, pp. 248-285.||2|
|MERMET, L (1992). Stratégies pour la gestion de l'environnement, la nature comme jeu de société? Paris: L'Harmattan.||2|
|LANSING, J S (2002). Artificial Societies and the Social Sciences. Artificial Life, 8(3), 1 pp. 279-292(14)||2|
|KOHLER, T A (1999). Putting social sciences together again: an introduction to the volume. In Kohler, T A and Gumerman, G J (Ed.): Dynamics in human and primate societies. Santa Fe Institute.||2|
|JANSSEN, M (Ed.) (2002). Complexity and Ecosystem Management. Cheltenham: Edward Elgar Publishing.||1|
|BARRETEAU, O and BOUSQUET, F (1999). Jeux de rôles et validation de systèmes multi-agents. In Gleizes, M P and Marcenac, P (Eds.): Ingénierie des systèmes multi-agents. Actes des 7èmes JFIADSMA, Hermès, pp. 67-80.||1|
|5a Norms / Altruistic Behavior (description) (picture) |
D = 0.46
|HALES, D (2002). Group Reputation Supports Beneficent Norms. Journal of Artificial Societies and Simulation, Vol 5, No. 4.||6|
|CASTELFRANCHI, C, CONTE, R and PAOLUCCI, M (1998). Normative Reputation and the Costs of Compliance. Journal of Artificial Societies and Social Simulation, Vol. 1, No. 3.||5|
|MAUSS, M (1990). The Gift: The Form and Reason for Exchange in Archaic Societies. New York: Norton Press.||5|
|YOUNGER, S M (2004). Reciprocity, Normative Reputation, and the Development of Mutual Obligation in Gift-Giving Societies", Journal of Artificial Societies and Social Simulation, Vol. 7, No. 1.||4|
|SAAM, N J and HARRER, A (1999). Simulating Norms, Social Inequality, and Functional Change in Artificial Societies. Journal of Artificial Societies and Social Simulation, Vol. 2, No. 1.||2|
|JAFFE, K (2002). An Economic Analysis of Altruism: Who Benefits from Altruistic Acts? Journal of Artificial Societies and Social Simulation, Vol. 5, No. 3.||2|
|YOUNGER, S M (2003). Discrete Agent Simulations of the Effect of Simple Social Structures on the Benefits of Resource Sharing. Journal of Artificial Societies and Social Simulation, Vol. 6, No. 3.||2|
|MALSCH, T (2001). Naming the Unnamable: Socionics or the Sociological Turn of-to Distributed Artificial Intelligence. Autonomous Agents and Multi-agent Systems, Vol. 4, No. 3, pp. 155-186.||1|
(undescribed branch) (picture)
|PARSONS, T (1951). The Social System. New York: Free Press.||2|
|LUHMANN, N (1984). Soziale Systeme. Grundriß einer allgemeinen Theorie. Frankfurt a. M.: Suhrkamp.||2|
|DITTRICH, P, KRON, T and BANZHAF, W (2003). On the Formation of Social Order: Modeling the Problem of Multi and Double Contingency following Luhmann. Journal of Artificial Societies and Social Simulation, Vol. 6, No. 1.||2|
|SHANNON, C E (1948). A Mathematical Theory of Communication. Bell System Technical Journal, Vol. 27, pp. 379-423 and 623-356.||1|
|6 Behavioral Economics / Artificial Markets (description) (picture) |
|AXTELL, R (2000). Why agents? On the varied motivations for agent computing in the social sciences. Center for Social and Economic Dynamics, Working Paper No. 17.||6|
|CHIARELLA, C (1992). The dynamics of speculative behaviour. Annals of Operations Research, Vol. 37, pp.101-123.||5|
|MINAR, N, BURKHARD, R, LANGTON, C and ASKENAZIM, M (1996). The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations. Overview paper. Santa Fe Institute, Santa Fe, NM.||3|
|ARIFOVIC, J (1996). The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy, Vol. 104, No. 3, pp. 510-541.||2|
|ARTHUR, W B, HOLLAND, J, LEBARON, B and PALMER, R T P (1997). Asset pricing under endogenous expectations in an artificial stock market. In Arthur, W B, Durlauf, S and Lane, D (Ed.): The economy as an evolving complex system II. Reading, MA: Addison Wesley. pp. 15-44||2|
|KAHNEMAN, D and TVERSKY, A (1979). Prospect theory: an analysis of decision under risk. Econometrica, Vol. 47, pp. 263-291.||2|
|KAHNEMAN, D and TVERSKY, A (2000). Choices, Values and Frames. Cambridge: Cambridge University Press.||2|
|SHLEIFER, A (2000). Inefficient Markets. Oxford: Oxford University Press.||2|
|GODE, D K and SUNDER, S (1993). Allocative efficiency of markets with zero intelligence traders. Journal of Political Economy, Vol. 101, pp. 119-37.||2|
|AXELROD, R. (1997). The Complexity of Cooperation. Princeton: University Press.||1|
|LUNA F, STEFANSSON B (Eds.) (2000). Economic Simulation in Swarm: Agent-Based Modelling and Object-Oriented Programming. Boston/Dordrecth/London: Kluwer Academic Publishers.||1|
|7 Evolution and Learning in Social Dilemmas (description) (picture) |
D = 0.29
|DAWES, R M (1980). Social Dilemmas. Annual Review of Psychology, Vol. 31, pp. 161-193.||3|
|GOTTS, N M, POLHILL, J G and LAW, A N R (2003). Agent-Based Simulation in the Study of Social Dilemmas. Artificial Intelligence Review, Vol. 19, No.1, pp. 3-92.||3|
|MACY, M W and FLACHE, A (2002). Learning Dynamics in Social Dilemmas. Proceedings of the National Academy of Sciences, Vol. 99, No. 10, pp. 7229-7236.||2|
|WEIBULL, J W (1995). Evolutionary Game Theory. Cambridge, MA: MIT Press.||1|
|BAK, P (1996). How Nature Works: The Science of Self-Organized Criticality. Oxford: Oxford University Press.||1|
|CIOFFI-REVILLA, C (2002). Invariance and universality in social agent-based simulations. Proceedings of the National Academy of Sciences of the United States of America, Vol. 99, No.3, pp. 7314-7316.||1|
|ROTH, A E and EREV, I (1995). Learning in extensive form games: experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior, Vol. 8, pp. 164-21.||1|
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