A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)

In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose

COllective INtelligence with task assignment

In this paper we study the COllective INtelligence (COIN) framework of Wolpert et al. for dispersion games (Grenager, Powers and Shoham, 2002) and variants of the EL Farol Bar problem. These settings constitute difficult MAS problems where fine-grained coordination between the agents is required. We enhance the COIN framework to dramatically improve convergence results for MAS with a large number of agents. The increased convergence properties for the dispersion games are competitive with especially tailored strategies for solving dispersion games. The enhancements to the COIN framework proved to be essential to solve the more complex variants of the El Farol Bar-like problem

Complex network analysis and nonlinear dynamics

This chapter aims at reviewing complex network and nonlinear dynamical models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary introduces some applications of complex networks to economics, finance, epidemic spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issue

Fairness Emergence in Reputation Systems

Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication

In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel. To this end, we introduce the Graphical Referential Game (GREG) where a speaker must produce a graphical utterance to name a visual referent object while a listener has to select the corresponding object among distractor referents, given the delivered message. The utterances are drawing images produced using dynamical motor primitives combined with a sketching library. To tackle GREG we present CURVES: a multimodal contrastive deep learning mechanism that represents the energy (alignment) between named referents and utterances generated through gradient ascent on the learned energy landscape. We demonstrate that CURVES not only succeeds at solving the GREG but also enables agents to self-organize a language that generalizes to feature compositions never seen during training. In addition to evaluating the communication performance of our approach, we also explore the structure of the emerging language. Specifically, we show that the resulting language forms a coherent lexicon shared between agents and that basic compositional rules on the graphical productions could not explain the compositional generalization

Collaborative action research for the governance of climate adaptation - foundations, conditions and pitfalls

This position paper serves as an introductory guide to designing and facilitating an action research process with stakeholders in the context of climate adaptation. Specifically, this is aimed at action researchers who are targeting at involving stakeholders and their expert knowledge in generating knowledge about their own condition and how it can be changed. The core philosophy of our research approach can be described as developing a powerful combination between practice-driven collaborative action research and theoretically-informed scientific research. Collaborative action research means that we take guidance from the hotspots as the primary source of questions, dilemmas and empirical data regarding the governance of adaptation, but also collaborate with them in testing insights and strategies, and evaluating their usefulness. The purpose is to develop effective, legitimate and resilient governance arrangements for climate adaptation. Scientific quality will be achieved by placing this co-production of knowledge in a well-founded and innovative theoretical framework, and through the involvement of the international consortium partners. This position paper provides a methodological starting point of the research program ‘Governance of Climate Adaptation’ and aims: · To clarify the theoretical foundation of collaborative action research and the underlying ontological and epistemological principles · To give an historical overview of the development of action research and its different forms · To enhance the theoretical foundation of collaborative action research in the specific context of governance of climate adaptation. · To translate the philosophy of collaborative action research into practical methods; · To give an overview of the main conditions and pitfalls for action research in complex governance settings Finally, this position paper provides three key instruminstruments developed to support Action Research in the hotspots: 1) Toolbox for AR in hotspots (chapter 6); 2) Set-up of a research design and action plan for AR in hotspots (chapter 7); 3) Quality checklist or guidance for AR in hotspots (chapter 8)