“It Takes All Kinds”: A Simulation Modeling Perspective on Motivation and Coordination in Libre Software Development Projects

This paper presents a stochastic simulation model to study implications of the mechanisms by which individual software developers’ efforts are allocated within large and complex open source software projects. It illuminates the role of different forms of “motivations-at-the-margin” in the micro-level resource allocation process of distributed and decentralized multi-agent engineering undertakings of this kind. We parameterize the model by isolating the parameter ranges in which it generates structures of code that share certain empirical regularities found to characterize actual projects. We find that, in this range, a variety of different motivations are represented within the community of developers. There is a correspondence between the indicated mixture of motivations and the distribution of avowed motivations for engaging in FLOSS development, found in the survey responses of developers who were participants in large projects.free and open source software (FLOSS), libre software engineering, maintainability, reliability, functional diversity, modularity, developers’ motivations, user-innovation, peer-esteem, reputational reward systems, agent-based modeling, stochastic simulation, stigmergy, morphogenesis.

Computational rationality and voluntary provision of public goods: an agent-based simulation model

The issue of the cooperation among private agents in realising collective goods has always raised problems concerning the basic nature of individual behaviour as well as the more traditional economic problems. The Computational Economics literature on public goods provision can be useful to study the possibility of cooperation under alternative sets of assumptions concerning the nature of individual rationality and the kind of interactions between individuals. In this work I will use an agent-based simulation model to study the evolution of cooperation among private agents taking part in a collective project: a high number of agents, characterised by computational rationality, defined as the capacity to calculate and evaluate their own immediate payoffs perfectly and without errors, interact to producing a public good. The results show that when the agents’ behaviour is not influenced either by expectations of others’ behaviour or by social and relational characteristics, they opt to contribute to the public good to an almost socially optimal extent, even where there is no big difference between the rates of return on the private and the public investment.Computational Economics; Agent-based models; Social Dilemmas; Collective Action; Public Goods

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

The evolution of morality and the end of economic man

1871 saw the publication of two major treatises in economics, with self-seeking economic man at their center. In the same year Darwin published The Descent of Man, which emphasized sympathy and cooperation as well as self-interest, and contained a powerful argument that morality has evolved in humans by natural selection. Essentially this stance is supported by modern research. This paper considers the nature of morality and how it has evolved. It reconciles Darwin's notion that a developed morality requires language and deliberation (and is thus unique to humans), with his other view that moral feelings have a long-evolved and biologically-inherited basis. The social role of morality and its difference with altruism is illustrated by an agent-based simulation. The fact that humans combine both moral and selfish dispositions has major implications for the social sciences and obliges us to abandon the pre-eminent notion of selfish economic man. Economic policy must take account of our moral nature.Peer reviewedFinal Accepted Versio

The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling

Better Be Convincing or Better Be Stylish? a Theory Based Multi-Agent Simulation to Explain Minority Influence in Groups Via Arguments or Via Peripheral Cues

Very often in the history of mankind, social changes took place because a minority was successful in persuading the dominant majority of a new idea. Social psychology provides empirically well-founded theories of social influence that can explain the power of minorities at individual level. In this contribution, we present an agent-based computer simulation of one such theory, the Elaboration Likelihood Model (ELM). After introducing the theoretical background and our agent model, we present three simulation experiments that confirm past laboratory research but also go beyond its findings by adopting the method of computer simulation. First, we found that even a minority with low argument quality can be successful as long as it has positive peripheral cues. Second, our results suggest that a higher personal relevance of a topic for the majority led it to be more receptive to minority influence only when the minority showed neutral peripheral cues and very good arguments. Third, we found evidence that a neutral or only slightly biased majority is influenced more easily than a strongly biased one. To sum up, we consider these results to illustrate the notion that a well-presented, comprehensible and valid computer simulation provides a useful tool for theory development and application in an exploratory manner as long as it is well founded in terms of the model and theory.Social Influences, Persuasion Processes, Group Processes, Minority Influence, Computer Simulation, Modelling, Theory Verification, Simulation Experiments

Noncooperative Support of Public Norm Enforcement in Large Societies

In small groups norm enforcement is provided by mutual punishment and reward. In large societies we have enforcement institutions. This paper shows how such institutions can emerge as a decentralized equilibrium. In a first stage, individuals invest in a public enforcement technology. This technology generates a sanctioning system whose effectiveness depends on the aggregate amount of invested resources. In a second stage, in which individuals contribute to the provision of a public good, the sanctioning system imposes penalties and rewards on deviations from the endogenous norm contribution. It is shown that even if group size goes to infinity public norm enforcement is supported in a noncooperative equilibrium. Psychological factors are not necessary but can be favorable for the emergence of effective public norm enforcement.norm enforcement, public goods, institutions, sanctioning

Opinion dynamics with varying susceptibility to persuasion

A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates