Luis Antunes

Multi-Agent-Based Simulation VII

Invited Papers.- Exploring the Vast Parameter Space of Multi-Agent Based Simulation.- Applications of Agent Based Simulation.- Empirical Cross Studies.- Analyzing Dynamics of Peer-to-Peer Communication -From Questionnaire Surveys to Agent-Based Simulation.- Modeling Human Education Data: From Equation-Based Modeling to Agent-Based Modeling.- Experimental Ecology.- Contrasting a System Dynamics Model and an Agent-Based Model of Food Web Evolution.- Roost Size for Multilevel Selection of Altruism Among Vampire Bats.- Experimental Economics.- Tactical Exploration of Tax Compliance Decisions in Multi-agent Based Simulation.- Learning to Use a Perishable Good as Money.- Foundations and Methodologies.- A Holonic Approach to Model and Deploy Large Scale Simulations.- Concurrent Modeling of Alternative Worlds with Polyagents.- Learning.- Integrating Learning and Inference in Multi-agent Systems Using Cognitive Context.- Can Agents Acquire Human-Like Behaviors in a Sequential Bargaining Game? - Comparison of Roths and Q-Learning Agents -.- Social Dependence.- Quantifying Degrees of Dependence in Social Dependence Relations.

Tax compliance in a simulated heterogeneous multi-agent society

We consider an individualised approach to agent behaviour in an application to the classical economic problem of tax compliance. Most economic theories consider homogeneous representative agent utilitarian approaches to explain the decision of complying or not with tax payment. However, a heterogeneous and individualised account of decision can be considered to explain certain apparently irrational behaviours. Ideas such as trust and peer perception may have a key influence in individual decisions, and thus transform the global results for society. In this paper, we apply the agent view of rationality to economic decisions and define a territory to be explored by agent technology and social simulations. We conclude that the multi-agent view can provide powerful results which might lead to significant economic policy implications.

Improving Choice Mechanisms within the BVG Architecture

The BVG agent architecture relies on the use of values (multiple dimensions against which to evaluate a situation) to perform choice among a set of candidate goals. Choice is accomplished by using a calculus to collapse the several dimensions into a function that serialises candidates. In our previous experiments, we have faced decision problems only with perfect and complete information. In this paper we propose new experiments, where the agents will have to decide in the absence of all the needed and relevant information. In the BVG model, agents adjust their scale of values by feeding back evaluative information about the consequences of their decisions. We use the exact same measures to analyse the results of the experiments, thus providing a fair trial to the agents: they are judged with the same rules they can use for decision. Our method, based on values, is a novel approach for choice and an alternative to classical utilitarian theories.

Tactical exploration of tax compliance decisions in multi-agent based simulation

Tax compliance is a field that crosses over several research areas, from economics to machine learning, from sociology to artificial intelligence and multi-agent systems. The core of the problem is that the standing general theories cannot even explain why people comply as much as they do, much less make predictions or support prescriptions for the public entities. The compliance decision is a challenge posed to rational choice theory, and one that defies the current choice mechanisms in multi-agent systems. The key idea of this project is that by considering rationally-heterogeneous agents immersed in a highly social environment we can get hold of a better grasp of what is really involved in the individual decisions. Moreover, we aim at understanding how those decisions determine tendencies for the behaviour of the whole society, and how in turn those tendencies influence individual behaviour. This paper presents the results of some exploratory simulations carried out to uncover regularities, correlations and trends in the models that represent first and then expand the standard theories on the field. We conclude that forces like social imitation and local neighbourhood enforcement and reputation are far more important than individual perception of expected utility maximising, in what respects compliance decisions.

Multi-Agent-Based Simulation VIII

MABS Celebrates Its 10th Anniversary!.- MABS Celebrates Its 10th Anniversary!.- Architectures.- System Issues in Multi-agent Simulation of Large Crowds.- Middleware Support for Performance Improvement of MABS Applications in the Grid Environment.- E Pluribus Unum: Polyagent and Delegate MAS Architectures.- A Multi-agent Model for the Micro-to-Macro Linking Derived from a Computational View of the Social Systems Theory by Luhmann.- Teams, Learning, Education.- Agent-Based Simulation of Group Learning.- An Agent-Based Model That Relates Investment in Education to Economic Prosperity.- Economy, Trust and Reputation.- Trust-Based Inter-temporal Decision Making: Emergence of Altruism in a Simulated Society.- Multi-agent Model of Technological Shifts.- Beyond Accuracy. Reputation for Partner Selection with Lies and Retaliation.

e*plore v.0: Principia for Strategic Exploration of Social Simulation Experiments Design Space

Years ago, we addressed the issue of methodological procedures to develop the design of cognitive agents tuned to real problems, inserting them into a context where experimentation could have a meaningful outcome in terms of the original problems posed. Since then we have been building mechanisms and frameworks for mind design in multiagent systems. We proceeded with the evaluation of such systems through simulation that was many times exploratory. In many of those experiments, the evaluation of the deep meaning of outcomes was inherently complex, challenging the researchers and even the research questions.

Force Versus Majority: A Comparison in Convention Emergence Efficiency

In open societies such as multi-agent systems, it is important that coordination among the several actors is achieved efficiently. One economical way of capturing that aspiration is consensus: social conventions and lexicons are good examples of coordinating systems, where uniformity promotes shared expectations of behavior and shared meanings. We are particularly interested in consensus that is achieved without any central control or ruling, through decentralized mechanisms that prove to be effective, efficient, and robust. The nature of interactions and also the nature of society configurations may promote or inhibit consensual emergence. Traditionally, preference to adopt the most seen choices (the majority option) has dominated the emergence convention research in multi-agents, being analyzed along different social topologies. Recently, we have introduced a different type of interaction, based on force, where force is not defined a priori but evolves dynamically. We compare the Majority class of choice update against Force based interactions, along three dimensions: types of encounters, rules of interaction and network topologies. Our experiments show that interactions based on Force are significantly more efficient (fewer encounters) for group decision making.

Context Switching versus Context Permeability in Multiple Social Networks

In social life, actors engage simultaneously in several relations with each other. The complex network of social links in which agents are engaged is fundamental for any realistic simulation of social life. Moreover, not only the existence of multiple-modality paths between agents in a simulation, but also the knowledge that those agents have about the quality and specificity of those links are relevant for the decisions the agents make and the consequences they have both at an individual and at a collective level. Each actor has a context in each of the relations that are represented as support of a simulation. We build on previous work about permeability between those contexts to study the novel notion of context switching. By switching contexts, individuals carry with them their whole set of personal characteristics to a different relation, while abandoning the previous one. When returning to the original context, all previous links are resumed. We apply these notions to a simple game: the consensus game, in which agents try to collectively achieve an arbitrary consensus through simple locally informed peer-to-peer interactions. We compare the results for context switching with results from simulating the simple game and the game with context permeability.

Mentat: a data-driven agent-based simulation of social values evolution

This work presents an agent based simulation model dealing With the evolution of social values in a 20 year period of the Spanish society, approaching it from Ingleharts theories on the subject. Surveys are taken as input to build the model by following a data-driven approach. This has been formalised in a methodology for introducing microsimulation techniques and importing data from several sources. It handles thousands of heterogeneous agents, which have a life cycle, reproduction patterns and complex social relationship dynamics. Its output is consistent with respect to the ideological, religious and demographic parameters observed in real world surveys. Moreover, several extension modules were designed: fuzzy logic for a smoother behaviour; natural language biographies generation; data mining for pattern finding. Thus, Mentat is proposed as a framework for exploring complexity at different levels in the social process.

Agents that collude to evade taxes

We explore the link between micro-level motivations leading to and being influenced by macro-level outcomes to study the complex issue of tax evasion. If it is obvious why there is a benefit for people who evade taxes, it is less obvious why people would pay any taxes at all, given the the small probability of being caught, and the small penalties involved. We use exploratory simulation and progressively deepening models of agents and of simulations to study the reasons behind tax evasion. We have unveiled some relatively simple social mechanisms that can explain the compliance numbers observed in real economies. We claim that simulation with multiple agents provides a strong methodological tool with which to support the design of public policies.