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The primary goal of this book is to present the research findings and conclusions of physicists, economists, mathematicians and financial engineers working in the field of "Econophysics" who have undertaken agent-based modelling, comparison with empirical studies and related investigations. Most standard economic models assume the existence of the representative agent, who is "perfectly rational" and applies the utility maximization principle when taking action. One reason for this is the desire to keep models mathematically tractable: no tools are available to economists for solving non-linear models of heterogeneous adaptive agents without explicit optimization. In contrast, multi-agent models, which originated from statistical physics considerations, allow us to go beyond the prototype theories of traditional economics involving the representative agent. This book is based on the Econophys-Kolkata VII Workshop, at which many such modelling efforts were presented. In the book, leading researchers in their fields report on their latest work, consider recent developments and review the contemporary literature

Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. Here Michael Laver and Ernest Sergenti offer the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues.

Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated "diversity trumps ability" result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits of democracy. In particular we argue that the "best-performing agents" in the Hong-Page model should not be interpreted as experts. We next explore a closely related model in which best-performing agents are more plausibly seen as experts and show that the diversity trumps ability result fails to hold. However, with changes in other parameters (such as the deliberation dynamic) the diversity trumps ability result is restored. The sensitivity of this result to parameter choices illustrates the complexity of the link between formal modeling and more general philosophical claims; we use this debate as a platform for a more general discussion of when and how agent-based models can contribute to philosophical discussions.

"Agent-Based Modeling in Political Decision Making" published on by Oxford University Press.

In this paper, we present an abstract argumentation framework for the support of agreement processes in agent societies. It takes into account arguments, attacks among them, and the social context of the agents that put forward arguments. Then, we de¿ne the semantics of the framework, providing a mechanism to evaluate arguments in view of other arguments posed in the argumentation process. We also provide a translation of the framework into a neural network that computes the set of acceptable arguments and can be tuned to give more or less importance to argument attacks. Finally, the framework is illustrated with an example in a real domain of a water-rights transfer market. & 2011 Elsevier B.V. All rights reserved ; This work is supported by the Spanish government Grants CONSOLIDER INGENIO 2010 CSD2007-00022, TIN2008-04446 and TIN2009-13839-C03-01 and by the GVA project PROMETEO 2008/051. ; Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2012). Argument-based agreements in agent societies. Neurocomputing. 75(1):156-162. https://doi.org/10.1016/j.neucom.2011.02.022 ; S ; 156 ; 162 ; 75 ; 1

The notion of transition receives noticeable attention in political as well as scientific arenas. In the policy arena in the Netherlands, significant results have not been achieved yet despite all the efforts on 'managing' the transition to a sustainable energy sector. Although the scientific literature on transitions contains publications from 25 countries, the US was most important before 2000 and authors from the Netherlands have been dominant since. Simulations of energy transitions are in its early stages, compared to their potential. Our bibliographical analysis of the transition literature shows that the number of papers mentioning simulations is low, only 19 out of 142, and their young. Most of those papers describe case studies that focus on autonomous, unmanaged transitions; only a few aim their simulations at transition management. Complex systems theory tells us that energy infrastructures – true socio-technical systems – cannot be designed. Therefore, transition management is a paradox: when transitions are expected to take decades, how could we know what actions to take now in order to shape energy infrastructures in such a way that the preferred transition will occur over decades? And at the end of the day, how could we attribute the result to transition management activities, whether the transition was successful or not? This paradox is no argument to wait: policy issues regarding energy infrastructures have to be made today. Therefore, we have set out for simulations of energy transition, using agent-based models, as to support energy transition management. Results from three cases – regarding CO2 reduction from power generation, the electricity-intensity of consumer lighting, and a spot market for LNG trade – have proved that it is possible to gain useful insights in how the myriad of decisions made in energy infrastructures can be influenced in a way that a transition is likely. ; Infrastructures, Systems and Services ; Technology, Policy and Management