Papers Presented at the Model to Model Worshop

Concerns about genetic selection as a template for models of cultural adaptation have led cognitive game theorists to explore learning-theoretic alternatives. Two prominent examples are the Bush-Mosteller stochastic learning model and the Roth-Erev payoff-matching model. We align and integrate the two models as special cases of a General Reinforcement Learning Model. Both models predict stochastic collusion as a backward-looking solution to the problem of cooperation in social dilemmas, based on a random walk into a self-reinforcing cooperative equilibrium. The integration also uncovers hidden assumptions that constrain the generality of the theoretical derivations. Specifically, Roth and Erev assume a 'Power Law of Learning' - the curious but plausible tendency for learning to diminish with success and intensify with failure, which we call 'fixation.' We use computer simulation to explore the effects of fixation on stochastic collusion in three social dilemma games. The analysis shows how the integration of alternative models can uncover underlying principles and lead to a more general theory.

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Computational Models and Multi-level Experimental Data: A Study of Behavior in Public-Goods Provision Experiments
Marco A. Janssen and T.K. Ahn (Indiana University, USA)

Abstract

In this paper we compare an experience-weighted attraction (EWA) learning model and a best-response with signaling (BRS) model in the context of linear public good games. The BRS model performs better in both the individual and aggregate-level calibrations. Another main …nding of the study is that incorporating heterogeneous preferences aspiration levels substantially enhance understanding of behavior in such games. Many studies use learning models in which players their behavior based on the past experiences. These models have made significant progresses in understanding behavior in relatively simple games. In public good games, however, the behavior of players do not converge to the zero-contribution equilibrium, thus posing a challenge to the learning models. We find that modeling individuals less selfish, more rational, more satisficing, and most of all, heterogenous on multiple levels, leads to a step forward in explaining behavior and dynamics in public good games. In general this paper shows that there are interesting possibilities and challenges in combining experimental research and agent-based models in comparing alternative models of human behavior in complex social dilemmas.

Comparative analysis of agent-based social simulations: GeoSim and FEARLUS models
Claudio Cioffi-Revilla (George Mason University, USA) and Nicholas M. Gotts (Macaulay Institute, Scotland)

Abstract

In this paper we compare models of two different kinds of processes in multi-agent-based social simulations (MABSS): military conflict within a states-system (GeoSim), and land use and ownership change (FEARLUS). This is a kind of model-to-model comparison which is novel within MABSS research, although well-known within mathematics, physics and biology: comparing objects (in this case MABSS) drawn from distinct research domains, in order to draw out their structural similarities and differences. This can facilitate research in both domains, by allowing the use of findings from each to illuminate the other. Based on the similarities between FEARLUS and GeoSim, we conclude by identifying a new class of MABS (multi-agent-based simulation) models based on territorial resource allocation processes occurring on a 2-dimensional space (which we define as the “TRAP²” class). The existence of the cross-domain TRAP² class of models in turn suggests that MABS researchers should look for other members of the class, sharing some of the properties or dynamics common to the GeoSim and FEARLUS models compared in this study: a systematic comparison of a set of related models from a range of apparently distinct domains should generate insights into both MABS modeling, and the domains concerned.

Simulations of Group Dynamics with Different Models
Jürgen Klüver and Christina Stoica (University of Essen, Germany)

Abstract

A socio-matrix or Moreno matrix respectively describes relations between members of a group. Such a matrix can also be used to predict the dynamics of a group, i.e., the behaviour of the group members is determined by the values of the matrix. Several different models are used to analyse the group dynamics based on Moreno matrices, namely a cellular automaton (CA), a Kohonen feature map (KFM), an interactive neural net (IN), and a genetic algorithm (GA). The results of the different models are compared; the models produce rather similar effects. In addition the predictions of the models are compared with empirical observations with respect to groups of students and children in a summer camp. The models are quite efficient in predicting real social behaviour.

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Individual-based model to enrich an aggregate model
Raphaël Duboz, Frédéric Amblard, Eric Ramat, Guillaume Deffuant, Philippe Preux (Laboratoire d'Informatique du Littoral and Cemagref, France)

Abstract

We propose to use individual-based models as virtual laboratories to identify parameters of aggregate models. We illustrate this approach with a simple example: the diffusion of particles activated by a Brownian movement. We thus have an aggregated numerical model, as well as an individual-based model of this phenomenon. We consider various alternatives of this example, which lead to different use of the virtual laboratory. We consider in particular the coupling between the two models in which the aggregate model asks for calculations of parameters to the individual-based model. We evoke finally the case of scale shifts in the models and the way it can be managed by this type of approach.

Comparing individual-based model of behaviour diffusion with its mean field aggregated approximation
Margaret Edwards, Sylvie Huet, François Goreaud, Guillaume Deffuant (Cemagref, France)

Abstract

We compare the individual-based “threshold model” of innovation diffusion (Valente 95), in the version which has been studied by P. Young, and an aggregate deterministic model we constructed from it. The classical threshold model supposes that an individual adopts a behaviour according to a trade-off between a social pressure (the number of his neighbours adopting the behaviour) and a personal interest or resistance to change (the threshold). The aggregate model makes approximations in order to estimate the evolution of groups of individuals with the same number of neighbours of similar behaviour. We compare both models in different points of the parameter space. We find that the aggregate model gives a good approximation of the individual when the individual based model is very stochastic. When the individual based model is less stochastic, the behaviour of the aggregate approximation differs ; it is attracted by local equilibrium points which are not the most probable state (dominant equilibrium point) of the individual-based model. Our theoretical interpretation of this difference is based on a study of the attractors of both dynamics.

Re-implementing John Duffy’s model of speculative learning agents in a small scale society: Problems, interest and issues
Juliette Rouchier (GREQAM, Marseille, France)

Abstract

The paper presents an attempt at replication of a multi-agent model dealing with the issue of speculation. In the Journal of Economic Dynamics and Control, John Duffy presents his model and results, as a coupling between an experimental economic version and a multi-agent version, of a model by Kiyotaki and Wright (1989). This original model offers a structural setting on which to base a microeconomic view of speculation, by designing a production-exchange-consumption setting with three goods that differ by their storage costs. Here, I present my own version of the multi-agent model, which is as close as possible to John Duffy’s, although I have been unable to reproduce his actual results. Most of my results are neither close to the experimental data or the simulation data, which makes me discuss the model of rationality of agents itself, and the way the results were described. The replication process is all the more interesting that it allows to redefine the relevant indicators to analyze the model.

Abstract

A published simulation model (Riolo et al. 2001) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted – as with other kinds of experiment, simulations need to be independently replicated.