Research subjects

• Social simulation. I am particularly interested in applying dynamic modelling to generally static social psychology theories in order to study conditions of social change. 

• Systems study by modelling methodology. The double modelling (see figure 1. below), proposed and investigated by LISC, is a fantastic way to study complex systems.This approach, already used in ecology, can give an interesting insight into social simulation.

Figure 1. Double modelling

Main projects

• European project IMAGES 

• European project FIRMA

• BED

Publications

• 2008. Huet S. and Deffuant G., Bounded confidence with rejection: clusters or scattered opinions? accepted to ESSA 2008, September 1-5, Brescia, Italy, 11 pages.

• 2008. Huet S., Deffuant G., and W. Jager, 2008, Rejection Mechanism in 2D Bounded Confidence Provides More Conformity. Advances in Complex Systems, vol. 11, Issue 4, pp. 1-21, 2008, revised and completed version of the paper presented to ECCS'07.

• 2008. Huet S. and G. Deffuant, Bounded Confidence with Rejection: The Infinite Population Limit with Perfect Uniform Initial Density, MASHS 2008 (Modèles et Apprentissages en Sciences Humaines et Sociales), Créteil, 5-6 juin

• 2008. Huet S. and G. Deffuant, Differential equation models derived from an individual-based model can help to understand emergent effects, Journal of Artificial Societies and Social Simulation, vol. 11, pp. 21. http://jasss.soc.surrey.ac.uk/11/2/10.html

• 2008. Deffuant G., S. Huet,  S. Skerratt, 2008. An agent based model of agri-environmental measure diffusion: what for? S. Agent Based Modelling in Natural Resource Management,  A. Lopez Paredes, C. Hernandez Iglesias, Universidad de Valladolid INSISOC, p. 55 - 7319.

• 2007. Huet S., Deffuant G., Jager W. Des dynamiques de différenciation individuelles peuvent accroître le conformisme global. ARCo'07 Cognition - Complexité - Collectif, 28-30 novembre, Nancy (France)

• 2007. Huet S., Deffuant G., Jager W. Rejection Mechanism in 2D Bounded Confidence Provides More Conformity. European Conference on Complex Systems'07 (ECCS'07), Dresden, October 1-6 (Germany).

• 2007. Dubois E., Huet S., Deffuant G. Primacy Effect with Symmetric Features Propagating in a population. ESSA 2007, Toulouse, September.

• 2007. Deffuant G. and Huet S. Propagation Effect of Filtering Incongruent Information. Journal of Business Research, 60(8), August, 816-825.

• 2007. Huet S. and Deffuant G. When do interactions increase or decrease primacy bias. M2M Conference in Marseille, march 2007, 20 p.

• 2007. Huet S., Edwards M., Deffuant G. Taking into account the variations of social network in the mean-field approximation of the threshold behaviour diffusion model. Journal of Artificial Societies and Social Simulation), volume 10, Issue 1, january 2007 <http://jasss.soc.surrey.ac.uk/10/1/10.html>.

• 2006. Deffuant G. and Huet S. Collective Reinforcement of First Impression Bias. the First World Congress on Social Simulation, 21-25 August 2006, Kyoto (Japon), 10 pages.

• 2006. Huet S., Deffuant G. Effets d’un filtre cognitif sur la diffusion d’information. 6ème Conférence Francophone de Modélisation et Simulation. Modélisation, Optimisation et Simulation des Systèmes : Défis et Opportunités (MOSIM'06), 3-5 avril 2006, Rabat, Maroc, 10 pages.

• 2005. Huet S. Modélisation et exploration de modèles : modèle individus-centré versus modèle agrégé. Mémoire d'Ingénieur CNAM, option "Informatique, modélisation, optimisation", 146 pages.

• 2005. Deffuant, G., Huet, S., Amblard, F.. "An individual-based model of innovation diffusion mixing social value and individual benefit". American Journal of Sociology , 110-4, January 2005, pp.1041-1069.

• 2005. Edwards M., Ferrand N., Goreaud F. and Huet S. The relevance of aggregating a water consumption model cannot be disconnected from the choice of information available on the resource. Simulation Modelling Practice and Theory, volume 13, issue 4, June 2005, pp. 287-307.

• 2004. Huet S. Modéliser les interactions sociales par des réseaux d'automates à seuil. Approximation d'un réseau aléatoire d'automates binaires. Rapport de DEA "Informatique, Productique, Imagerie médicale - option Recherche opérationnelle et productique", Université de Clermont II, LIMOS.

• 2004. Huet S., Edwards M., Deffuant G. Taking into account the variations of social network in the mean-field approximation of the threshold behaviour diffusion model. ESSA Conference, Model to Model workshop, 16-19/09.2004, Valladolid, Spain.

• 2003. Edwards M., Huet S., Goreaud F., Deffuant G. Comparaison entre un modèle individu-centré de diffusion de l'innovation et sa version agrégée dérivée par champ moyen pour des simulations à court terme. Actes de MFI'03 "Modèles formels de l'interaction, Lille, mai 2003, 91-100.

• 2003. Edwards M., Huet S., Goreaud F., Deffuant G. Comparing individual-based model of behaviour diffusion with its mean field aggregated approximation. Colloque M2M 2003 et JASSS (Journal of Artificial Societies and Social Simulation), volume 6, Issue 4, october 2003,  http://jasss.soc.surrey.ac.uk/6/4/9.html.

• 2002. Deffuant G., Huet S., Bousset J.P., Henriot J., Amon G., Weisbuch G. - Agent-based simulation of organic farming conversion in Allier département. Complexity and Ecosystem Management, Marco A. Janssen Ed., pp. 158-187.

• 2001. Huet S., Deffuant G. - Rapport Calcul d'impact économique de la conversion à l'agriculture biologique (réalisé dans le cadre du projet Images).

• 2000. Deffuant G., Amblard F., Huet S., Bernard S., Ferrand N., Bousset J.P., Amon G., Henriot J., Gilbert N., Chattoe E., Weisbuch G.  - Simulation de l'évolution des conversions à l'agriculture biologique dans le département de l'Allier entre 1994 et 1999 par un modèle multi-agents de la population d'agriculteurs. Rapport intermédiaire.

• 2000.- Alvarez I., Huet S. - Rapport du projet européen BED

Thesis subject

Context of the research

Many Cemagref researches focus on the elaboration of more environment-friendly and more sustainable tools or practices. To be really efficient, these innovations have to be adopted by users. Therefore, the question of innovation diffusion is a real issue for sustainable development.

Researches about innovation diffusion have begun with G. Tarde in his classical work “The Laws of Imitation” (1890 in French, 1903 in English). Rogers (Rogers 1962 ) has formalised a descriptive paradigm, based on a series of empirical studies. Since then, researchers proposed different dynamic models of innovation diffusion. Considering assumptions about individual’s dynamics, two groups of models can be distinguished.

The first group of “threshold models” is initiated by Granovetter (Granovetter 1978) and synthesized by Valente (1995). The threshold is an individual’s attribute defined as the part of adopter in the individual’s reference social group which releases individual adoption. The social group can be the whole population, a sub-population or a neighbourhood in a social network (i.e. set of individuals linked to the considered individual). Adoption is, in this type of models, due to imitation which occurs more or less quickly according to the individual. For some researchers, (Blume 1993) , (Blume 1995) , (Young, 1998), the threshold is understood as an individual utility to adopt the innovation. Eventually, adoption depends on an individual interest (an economical impact, a preference, …) and on a social interest depending on the behaviour of the individual’s associates. Formally, these models are quite equivalent to those of ferromagnetism in physics (i.e. Ising’s model).

The second group of models has been developed during the European research project « Images » (Deffuant and al. 2001) . Inspired by “threshold models”, these models consider an individual explicit interest to adopt. This state does not depend directly on the observation of social group members’ states. It is defined by an expected object[1] utility computed by each individual from his/her opinions and knowledge. An individual is in relation with other individuals and discuss with them, possibly changing by this way his/her opinions and knowledge. In fact, during the discussions, he/she exchanges with others some pieces of information[2] and/or opinions about the object’s¹ features. The individual of this model possesses cognitive filters which allow him/her to more or less consider other’s opinions and/or information. These filters are based on the proximity between information and/or opinion and the individual way of self-defining. Several versions of these models (Deffuant, 2002); (Deffuant, Huet et al. 2005) ; (Deffuant, Huet et al. 2002) ; (Deffuant, Neau et al. 2001) ; (Deffuant, Weisbuch et al. 2003) , (Deffuant, 2006c) are based on general psychological assumptions: individual consider others’ opinions which are quite similar to his/hers, especially if they are more certain than his/hers (Urbig 2003) ; (Urbig and Lorenz 2004) and (Jager and Amblard 2004) proposed extensions of this model. More recently, we refined and studied the dynamics of information transmission: an individual considers only important information on object’s features and an information is more easily judged important in case it is congruent with individual’s attitude[3] about object (Huet, Deffuant, 2006 ; Deffuant, Huet 2006a, 2006b).

Contrary to other models in social sciences (Nowak and Vallacher 1998; Latané and Bourgeois 2001; Latané and Bourgeois 2001) , innovation diffusion models are not very well grounded in social and cognitive psychology, Yet, recent results about attitudes dynamics, and a variety of theories could give deep insight on innovation diffusion. The « Theory of Planned Behaviour » (Ajzen, 1985, 1987, 1988, 1991, 2002), which is a static model, comprising sub-models dedicated to information processing, to decision, seems particularly relevant.  These sub-models can be based on other important references: The Elaboration likelihood Model (Petty and Cacioppo 1981) considering attitude dynamic regarding individual information processing, the Expectancy-Value Model from Fishbein (Fishbein and Ajzen 1975; Eagly and Chaiken 1993; LittleJohn and Foss 2005) considering attitude dynamic in relation with beliefs,  and others …

PhD research subject

The main purpose of the PhD is to formulate a more relevant innovation diffusion model, based on an in-depth comparison between researches in social-psychology and existing models. Better grounded in the social sciences, we expect this new model to provide new global behaviours emerging from individuals dynamic and/or new individual dynamics. We shall follow the “Double modelling” approach (Deffuant, 2004) (Edwards, 2003a, 2003b) (Huet, Edwards et al. 2004) to elaborate this model. The principle of this method is to control the complexity of the individual-based model by checking very carefully the consequences of each feature on the global dynamics. Practically, it requires to perform well defined experimental designs on the individual based model, and to elaborate an approximate model of the global dynamics. The elaborated model will therefore be a trade-off between the probable complexity inspired by the social-psychology literature, and the modelling constraints.

Moreover, we shall check the practical use of the model on a use case focused on social representations of biodiversity. We shall consider more specific researches on environmental issues and values in social psychology (Schwartz, 1992), (Schultz 2000) ; (Schultz 2002) ; (Schultz 2001) ; (Schultz, Gouveia et al. 2005) ; (Schultz and Zelezny 2003) ; (Schultz and Zelezny 1999) , (Stern, Dietz et al. 1999) , (Schaller and Crandall 2004) , (Dietz, Fitzgerald et al. 2005) .

References

External references

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg: Springer.

Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 20, pp. 1-63). New York: Academic Press.

Ajzen, I. (1988). Attitudes, personality, and behavior. Milton-Keynes, England: Open University Press & Chicago, IL: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. Journal of Applied Social Psychology, 32, 665-683.

Blume, L. E. (1993). "The statistical mechanics of strategic interactions." Games And Economic Behavior 5: 387-424.

Blume, L. E. (1995). "The statistical mechanics of best-response strategy revision." Games and Economic Behavior 11: 111-145.

Deffuant, G. and e. al. (2001). "Rapport final du projet FAIR 3 2092 IMAGES : Modélisation de la diffusion de l'adoption de mesures agri-environnementales par les agriculteurs (1997-2001)."

Deffuant, G., S. Huet, et al. (2005). "An individual-based model of innovation diffusion mixing social value and individual payoff dynamics." American Journal of Sociology 110 (January)(4): 1041-1069.

Deffuant, G., S. Huet, et al. (2002). Agent-based simulation of organic farming conversion in Allier département. Complexity and Ecosystem Management. M. A. Janssen: 158-187.

Deffuant, G., D. Neau, et al. (2001). "Mixing beliefs among interacting agents." Advances in Complex Systems(3): 87-98.

Deffuant, G., G. Weisbuch, et al. (2003). "Simple is beautiful... and necessary." Journal of Artificial Societies and Social Simulation 6(1).

Dietz, T., A. Fitzgerald, et al. (2005). "Environmental values." Annual Reviews of Environment and Resources(30): 335-72.

Eagly, A. H. and S. Chaiken (1993). Combinatorial Models of Attitude Formation and Change. The Psychology of Attitudes. A. H. C. Eagly, S., Fort Worth, TX: Harcourt Brace Jovanovich, Thomson/Wadsworth. Chapter 5: 219-256.

Eagly, A. H. and S. Chaiken (1993). Future Directions in the Study of Attitudes. The Psychology of Attitudes. A. H. C. Eagly, S., Fort Worth, TX: Harcourt Brace Jovanovich, Thomson/Wadsworth. Chapter 14: 219-256.

Eagly, A. H. and S. Chaiken (1993). The Structures of Attitudes and Beliefs. The Psychology of Attitudes. A. H. C. Eagly, S., Fort Worth, TX: Harcourt Brace Jovanovich, Thomson/Wadsworth. Chapter 3: 89-154.

Fishbein, M. and I. Ajzen (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Reading MA: Addison-Wesley

Granovetter, M. (1978). "Threshold Models of Collective Behavior." American Journal of Sociology 83(6): 1420-1143.

Huet, S., M. Edwards, et al. (2004). Taking into account the variations of social network in the mean-field approximation of the threshold behaviour diffusion model. ESSA Conference, Model to Model (M2M), Valladolid, Spain.

Jager, W. and F. Amblard (2004). A dynamical perspective on attitude change. NAACSOS Conference 2004, June 27 - 29, 2004, Pittsburgh PA http://www.casos.cs.cmu.edu/naacsos/.

Latané, B. and M. J. Bourgeois (2001). Dynamic social impact and the consolidation, clustering, correlations and continuing diversity of culture (chapter 10). Group Processes: Blackwell handbook of social psychology. M. A. H. R. S. Tindale, Oxford: Blackwell: 235-258.

Latané, B. and M. J. Bourgeois (2001). Successfully Simulating Dynamic Social Impact: Three Levels of Prediction. Social Influence - Direct and Indirect Processes. J. P. Forgas and K. D. Williams, Psychology Press - Taylor & Francis Group. Part I. Social Influence: Fundamental Processes and Theories: 61-78.

LittleJohn, S. W. and K. A. Foss (2005). Theories of Human Communication, Eigth Edition, Thomson Wadsworth.

Nowak, A. and R. R. Vallacher (1998). Dynamical Social Psychology, The Guilford Press, 72 Spring Street, New York, NY 10012.

Petty, R. and J. Cacioppo (1981). Epilog: A General Framework for Understanding Attitude Change Processes. Attitudes and Persuasion: Classic and Contemporary Approaches. Wm. C. Brown. Dubuque, Iowa:: 254-269.

Rogers, E. M. (1962 ). Diffusion of innovations, The Free Press. New York.

Schaller, M. and C. S. Crandall (2004). The Psychological Foundations of Culture. Mahwah, New Jersey - London, Lawrence Erlbaum Associates.

Schultz, P. W. (2000). "Empathizing With Nature: The Effects of Perspective Taking on Concern for Environmental Issues - Statistical Data Included." Journal of Social Issues 56(3): 391-406.

Schultz, P. W. (2001). "The Structure of Environmental Concern: Concern for Self, Other People, and the Biosphere." Journal of Environmental Psychology 21: 327-339.

Schultz, P. W. (2002). "Environmental attitudes and behaviors across cultures." Online Readings in Psychology and Culture (Unit 8, Chapter 4) (www.wwu.edu/~culture), from www.wwu.edu/~culture.

Schultz, P. W., V. V. Gouveia, et al. (2005). "Values and their relationship to environmental concern and conservation behavior." Journal of Cross-Cultural Psychology 36(4): 457-475.

Schultz, P. W. and L. Zelezny (1999). "Values as predictors of environmental attitudes: evidence for consistency across 14 countries." Journal of Environmental Psychology 19: 255-265.

Schultz, P. W. and L. Zelezny (2003). "Reframing Environmental Messages to be Congruent with American Values." Human Ecology Review 10(2): 126-136.

Stern, P. C., T. Dietz, et al. (1999). "A value-Belief-Norm Theory of Support for Social Movements: The Case of Environmentalism." Human Ecology Review 6(2): 81-97.

Urbig, D. (2003). "Attitude Dynamics with Limited Verbalisation Capabilities." Journal of Artificial Societies and Social Simulation 6(1).

Urbig, D. and J. Lorenz (2004). Communication regimes in opinion dynamics: Changing the number of communicating agents. Second Conference of the European Social Simulation Association (ESSA),, Valladolid, Spain (September 16-19).

Young, P. (1998). Individual Strategy and Social Structure: An Evolutionary Theory of Institutions, Princeton University Press

Tarde G., 1890. Les lois de l’imitation. http://classiques.uqac.ca/classiques/tarde_gabriel/ lois_imitation/lois_imitation.html

Internal references

Deffuant, G. and e. al. (2001). "Rapport final du projet FAIR 3 2092 IMAGES : Modélisation de la diffusion de l'adoption de mesures agri-environnementales par les agriculteurs (1997-2001)."

Deffuant, G., D. Neau, et al. (2001). "Mixing beliefs among interacting agents." Advances in Complex Systems(3): 87-98.

Deffuant, G., F. Amblard, et al. (2002). "How can extremism prevail ? A study based on the relative agreement interaction model." Journal of Artificial Societies and Social Simulation 5(4).

Deffuant, G., S. Huet, et al. (2002). Agent-based simulation of organic farming conversion in Allier département. Complexity and Ecosystem Management. M. A. Janssen: 158-187.

Deffuant, G., G. Weisbuch, et al. (2003). "Simple is beautiful... and necessary." Journal of Artificial Societies and Social Simulation 6(1).

Deffuant G., (2004). Modéliser la complexité: quelques pistes pour relever le défi.  Mémoire d'Habilitation à Diriger des Recherches de l'Ecole doctorale SPI de l'université Blaise Pascal de Clermont-Ferrand, 133 pages, p. 106

Deffuant, G., S. Huet, et al. (2005). "An individual-based model of innovation diffusion mixing social value and individual payoff dynamics." American Journal of Sociology 110 (January)(4): 1041-1069.

Deffuant, G. and S. Huet (2006a). "Propagation effects of filtering incongruent information." Journal of Business Research accepted (to publish).

Deffuant G. and Huet S. (2006b) Collective reinforcement of first impression bias. Accepté à the First World Congress on Social Simulation, 21-25 August 2006, Kyoto (Japon), 10 pages

Deffuant G. (2006c). Comparing extremism propagation patterns in different continuous opinion models. To appear in Journal of Artificial Societies and Social Simulation.

Edwards, M., S. Huet, et al. (2003a). Comparaison entre un modèle individu-centré de diffusion de l'innovation et sa version agrégée dérivée par champ moyen pour des simulations à court terme. Modèles Formels d'Interactions (MFI'03), Lille (France), Cépaduès-éditions.

Edwards, M., S. Huet, et al. (2003b). "Comparing individual-based model of behaviour diffusion with its mean-field aggregated approximation." Journal of Artificial Societies and Social Simulation 6(4).

Huet, S., M. Edwards, et al. (2004). Taking into account the variations of social network in the mean-field approximation of the threshold behaviour diffusion model. ESSA Conference, Model to Model (M2M), Valladolid, Spain.

Huet, S. and G. Deffuant (2006a). Effets d'un filtre cognitif sur la diffusion d'information. 6ème Conférence Francophone de Modélisation et Simulation (MOSIM'06), Rabat (Maroc).