Multi-Agent Based Simulation XVIII

This paper presents a dramatic game for the self-regulation of social exchange processes in multi-agent systems, called Natyasastra, based on the concepts of Drama Theory. The model has five phases of dramatic resolution, which involve feelings, emotions, trust and reputation. Agents with different social exchange strategies interact among each other in order to maximize their strategy-based fitness functions. The objective is to obtain a more natural model than the ones existing in the literature, which are based either on (partially observable) Markov decision processes or in game theory, so that it can be applied in real-world applications. We aim at promoting more balanced and fair multi-agent interactions, increasing the number of successful social exchanges and, thus, promoting the continuity of social exchanges. The simulations showed that there is an improvement of fitness along time, as result of the self-regulation of the interactions. The agents have evolved their social exchange strategies, and other strategies, different from the original ones, have emerged in the society, so contributing to this evolution. This game was implemented in NetLogo.

Reputation may be understood as representing beliefs or opinions about someone or something, and it is recognized as a mechanism of social control. Reputation mechanisms are largely applied in online marketplaces, multiagent systems (MAS), P2P networks and other applications that require distributed and known information about agents. The process of evaluating agent’s reputation clearly involves imprecision, ambiguity and incompleteness. In this paper, we introduce a fuzzy logic-based reputation model for social exchange processes in MAS. We consider a variable dimensional system evaluation, using weighted aggregation functions in order to aggregate the fuzzy information of agent’s experiences (related to all considered dimensions) continuously, giving greater weight to more recent information. Some case studies are presented to analyse the behavior of the model. For that, we consider a MAS scenario in the context of online marketplace. We adopt the JaCaMo framework for the implementation, which uses BDI (Believe, Desires and Intentions) agent architecture and artifacts.

We consider an adaptation of Axelrod’s metanorm model, where a population of agents choose between cooperating and defecting in bilateral interactions. Because punishing incurs an enforcement cost, Axelrod proposes using metanorms, to facilitate the stability of a norm of punishing defectors, where those who do not punish defectors can themselves be punished. We present two approaches to study the social effects of such metanorms when agents can choose their interaction partners: (a) a theoretical study, when agent behaviors are static, showing stable social configurations, under all possible relationships between system parameters representing agent payoffs with or without defection, punishment, and meta-punishment, and (b) an experimental evaluation of emergent social configurations when agents choose behaviors to maximize expected utility. We highlight emergent social configurations, including anarchy, a “police” state with cooperating agents who enforce, and a unique “corrupt police” state where one enforcer penalizes all defectors but defects on others!

Multi-agent modeling is a computational approach to model behavior of complex systems in terms of simple micro level agent rules that result in macro level patterns and regularities. It has been argued that complex systems approaches provide distinct advantages over traditional equation-based mathematical modeling approaches in the process of scientific inquiry. We present a case study on how multi-agent modeling can be used to develop thought experiments in order to push theory forward. We develop a model of the evolution of gamete dimorphism (anisogamy), for which there are several competing theories in the evolutionary biology literature. We share the outcomes of our model and discuss how the model findings compare with, and contribute to previous work in the literature. The model clarifies mechanisms that can result in the evolution of anisogamy and offers a much simpler structure that is easier to understand, test, modify and extend.

This paper presents an overview of uncertainty assessment in agent-based simulations, mainly related to land use and cover change. Almost every multiagent-based simulation review has expressed the need for statistical methods to evaluate the certainty of the results. Yet these problems continue to be underestimated and often neglected. This work aims to review how uncertainty is being portrayed in agent-based simulation and to perform an exploratory study to use statistical methods to estimate uncertainty. MASE, a Multi-Agent System for Environmental simulation, is the system under study. We first identified the most sensitive parameters using Morris One-at-a-Time sensitivity analysis. The efforts to assess agent-based simulation through statistical methods are paramount to corroborate and improve the level of confidence of the research that has been made in land use simulation.

Social Simulations need agents with a realistic behavior to be used as a scientific tool by social scientists. When simulating a human society, a realistic behavior implies the use of cognition, social relations between people but also to take into account emotions and the dynamic between these features. However, developing such a behavior is often too complex for people with little knowledge in programming. In this paper, we present a formalism to represent cognition, social relations and emotions, which is integrated in an agent architecture to give a dynamic emotional behavior to social agents. This architecture is implemented in the open-source multi-agent platform GAMA. A use case about evacuation during bush fires in Australia is used to show the possibilities of our work.

This paper asks whether agent-based simulation can give insight into social factors surrounding corrupt behaviour in a technical process. The specific case study adopted, for studying the effects of social interconnectedness on corrupt behaviours, is the domain of maritime customs. Taking our previously-developed agent-based simulation, we add to the simulation a nuanced model of actor relatedness, consisting of clan, in-group (sect), and town of origin, and encode selected behavioural norms associated with these factors. Using the simulation, we examine the effects of social interconnectedness on domain performance metrics such as container outcomes, time, revenue, coercive demands, and collusion. Initial results confirm that as actor interconnectedness increases, established policies to combat corruption, such as process re-engineering, become less effective.

The computational modeling and simulation of social phenomena based on social theory as a theoretical framework is a challenging endeavor. Mainly, due to the difficulties to translate abstract conceptualizations of the social sciences into formal languages. The main goal of this paper is the translation of some Luhmann’s concepts such as perturbation, dissipation, social communication and power, into a model using a spatial social subsystem as a metaphor, to make more concrete these very abstract concepts. The model has been used to improve the social theory understanding and to evaluate the effect of different parameterization in the global stabilization and authorities’ distribution. It has been designed to comply with the Luhmann’s social theory, and to be scalable and simple to understand. The experiment implemented one instantiation of the proposed model and showed how it can be used to evaluate a micro-macro interaction based on a simple mechanism of Luhmannian social communication.

A model that describes the innovation process is developed in order to understand the effect of patent policies on an industry where firms interact under Schumpeterian competition. The technology space that is harvested by the firms in this particular case is a grid of \(200 \times 200\) sites, each site has a level of productivity and a resistance to be discovered and firms have the capacity to explore this space and imitate other discoveries locally and globally. However, even with such an appropriate scenario for a patent system, a negative effect was found on consumers and innovation due to the implementation of a strong patent system compared to the situation where there is no patent system.

A new approach is introduced under the slogan « Keep It a Learning Tool » (KILT) to emphasize the crucial need to make the purpose of the modelling process explicit when choosing the degree of complicatedness of an agent-based simulation model. We suggest that a co-design approach driven by early-stage and interactive simulation of empirical agent-based models representing stylized socio-ecosystems stimulates collective learning and, as a result, may promote the emergence of cooperative interactions among local stakeholders.

Multi-Agency problem occurs in Financial Markets, when multiple companies face agency problems at the same time, in different companies. It happens, when different shareholders’ types entering in conflict in order to maximise their benefits. This article explores the agency conflict between controlling shareholders and minor shareholders. This type of conflict arises from arbitrary power that sometimes major shareholders have, over small ones. In order to understand this multilateral conflict occurring in different companies, at the same time, it was created a multi-agent model where different agents’ type interact each other in an artificial financial market. The interaction occurs under the assumptions of a game theory, which means that multiple games happen among shareholders in different companies at the same time. In this specific study, we also added the agent who retaliates to the incursions of other agents. This article analyses the type of agents that constantly “wins the fights” in distinct scenarios previously simulated. After several simulations, it could be concluded that the initial structure of shareholders in a companies has impact in how the multiple games end up. Another important result that was achieved is about the gap between the value to be distributed among shareholders and consequent agency costs. Shareholders give more relevance to the value rather than agency costs that they can face out. This means that if the gap is negative because of the value, the shareholders may abandon the market and the same doesn’t happen, when it comes from a raise of the agency costs.

This paper presents a benchmark for multiagent systems specific to the simulator Soccerserver 2D, an environment to develop teams of robotic soccer, providing metrics and evaluation procedures for multiagent organization schemes, more specifically, coalitions formation. This benchmark has considered a MAS with two main levels, at least: (i) individual level, where agents are implemented from requisites of a social structure and considering its individual capabilities (roles, skills, etc); (ii) a social level, where all the social aspects of the MAS are specified (organization, plans, goals, etc.) and where the individual level of each agent instantiates these social knowledge to act in the system. The method proposed here has applied at social level, once it measures the quantity and quality of coalitions that arise in the environment.

This article explores the financial systemic risk emergence process using an agent-based simulation model representing the investor attitudes towards risk. The multidisciplinary theoretic base is compound of portfolio selection, sovereign debt securities and agent rationality literature. Following the 2007/8 world financial crisis, the sovereign debt crises in the European countries have been attracting researches, showing a “diabolic loop” between sovereign debt and the banking credit risk fragility, which can be followed by systemic crises. Modern financial systems rely heavily, mainly at times of political-economic uncertainty, on availability of safe assets (risk-free assets) to choose asset portfolios and also to use them as collateral in markets operations. In order to analyze the relations between financial rationality and investments on bonds of the Brazilian sovereign debt, this article uses a bottom-up approach, based on agent rationality, and simulates portfolio selection by neutrals, risk-seeking and risk-averse investors, all of them concrete classes of an investor abstract class. The main findings confirm that rational choices of investments are likely to be at the base of the doom loop that involves sovereign debt and institutional investors. The findings have important implications to policy makers regarding systemic risk issues, among others public policies.

Large cities around the world face numerous challenges to guarantee the quality of life of its citizens. A promising approach to cope with these problems is the concept of Smart Cities, of which the main idea is the use of Information and Communication Technologies to improve city services. Being able to simulate the execution of Smart Cities scenarios would be extremely beneficial for the advancement of the field. Such a simulator, like many others, would need to represent a large number of various agents (e.g. cars, hospitals, and gas pipelines). One possible approach for doing this in a computer system is to use the actor model as a programming paradigm so that each agent corresponds to an actor. The Erlang programming language is based on the actor model and is the most commonly used implementation of it. In this paper, we present the first version of InterSCSimulator, an open-source, extensible, large-scale Traffic Simulator for Smart Cities developed in Erlang, capable of simulating millions of agents using a real map of a large city. Future versions will be extended to address other Smart City domains.