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Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes?

Edmund Chattoe-Brown
Journal of Artificial Societies and Social Simulation 1 (3) 2

Abstract: This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.

Models of Social Influence: Towards the Next Frontiers

Andreas Flache, Michael Mäs, Thomas Feliciani, Edmund Chattoe-Brown, Guillaume Deffuant, Sylvie Huet and Jan Lorenz
Journal of Artificial Societies and Social Simulation 20 (4) 2

Abstract: In 1997, Robert Axelrod wondered in a highly influential paper "If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?" Axelrod’s question highlighted an ongoing quest for formal theoretical answers joined by researchers from a wide range of disciplines. Numerous models have been developed to understand why and under what conditions diversity in beliefs, attitudes and behavior can co-exist with the fact that very often in interactions, social influence reduces differences between people. Reviewing three prominent approaches, we discuss the theoretical ingredients that researchers added to classic models of social influence as well as their implications. Then, we propose two main frontiers for future research. First, there is urgent need for more theoretical work comparing, relating and integrating alternative models. Second, the field suffers from a strong imbalance between a proliferation of theoretical studies and a dearth of empirical work. More empirical work is needed testing and underpinning micro-level assumptions about social influence as well as macro-level predictions. In conclusion, we discuss major roadblocks that need to be overcome to achieve progress on each frontier. We also propose that a new generation of empirically-based computational social influence models can make unique contributions for understanding key societal challenges, like the possible effects of social media on societal polarization.

Role of Trust in a Self-Organizing Pharmaceutical Supply Chain Model with Variable Good Quality and Imperfect Information

Graeme J. Ackland, Edmund Chattoe-Brown, Heather Hamill, Kate R. Hampshire, Simon Mariwah and Gerry Mshana
Journal of Artificial Societies and Social Simulation 22 (2) 5

Abstract: We present an Agent-Based Model (hereafter ABM) for a pharmaceutical supply chain operating under conditions of weak regulation and imperfect information, exploring the possibility of poor quality medicines and their detection. Our interest is to demonstrate how buyers can learn about the quality of sellers (and their medicines) based on previous successful and unsuccessful transactions, thereby establishing trust over time. Furthermore, this network of trust allows the system itself to evolve to positive outcomes (under some but not all circumstances) by eliminating sellers with low quality products. The ABM we develop assumes that rational and non-corrupt agents (wholesalers, retailers and consumers) learn from experience and adjust their behaviour accordingly. The system itself evolves over time: under some - but not all - circumstances, sellers with low-quality products are progressively eliminated. Three distinct states of the supply chain are observed depending on the importance of trust built up from past experience. The 'dynamic' state is characterised by a low level of trust leading to a continually changing system with new drugs introduced and rejected with little regard to quality. The 'frozen' state arises from high levels of reliance on past experience and locks the supply chain into a suboptimal state. The 'optimising' state has moderate reliance on past experience and leads to the persistence of suppliers with good quality; however, the system is still 'invadable' by better quality drugs. Simulation results show that the state reached by the system depends strongly on the precise way that trust is established: Excessive levels of trust make it impossible for new, improved treatments to be adopted. This highlights the critical need to understand better how personal experience influences consumer behaviour, especially where regulation is weak and for products like medicines whose quality is not readily observable.

Different Modelling Purposes

Bruce Edmonds, Christophe Le Page, Mike Bithell, Edmund Chattoe-Brown, Volker Grimm, Ruth Meyer, Cristina Montañola-Sales, Paul Ormerod, Hilton Root and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 22 (3) 6

Abstract: How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.