Evolutionary establishment of moral and double moral standards through spatial interactions

Situations where individuals have to contribute to joint efforts or share scarce resources are ubiquitous. Yet, without proper mechanisms to ensure cooperation, the evolutionary pressure to maximize individual success tends to create a tragedy of the commons (such as over-fishing or the destruction of our environment). This contribution addresses a number of related puzzles of human behavior with an evolutionary game theoretical approach as it has been successfully used to explain the behavior of other biological species many times, from bacteria to vertebrates. Our agent-based model distinguishes individuals applying four different behavioral strategies: non-cooperative individuals ("defectors"), cooperative individuals abstaining from punishment efforts (called "cooperators" or "second-order free-riders"), cooperators who punish non-cooperative behavior ("moralists"), and defectors, who punish other defectors despite being non-cooperative themselves ("immoralists"). By considering spatial interactions with neighboring individuals, our model reveals several interesting effects: First, moralists can fully eliminate cooperators. This spreading of punishing behavior requires a segregation of behavioral strategies and solves the "second-order free-rider problem". Second, the system behavior changes its character significantly even after very long times ("who laughs last laughs best effect"). Third, the presence of a number of defectors can largely accelerate the victory of moralists over non-punishing cooperators. Forth, in order to succeed, moralists may profit from immoralists in a way that appears like an "unholy collaboration". Our findings suggest that the consideration of punishment strategies allows to understand the establishment and spreading of "moral behavior" by means of game-theoretical concepts. This demonstrates that quantitative biological modeling approaches are powerful even in domains that have been addressed with non-mathematical concepts so far. The complex dynamics of certain social behaviors becomes understandable as result of an evolutionary competition between different behavioral strategies.Comment: 15 pages, 5 figures; accepted for publication in PLoS Computational Biology [supplementary material available at http://www.soms.ethz.ch/research/secondorder-freeriders/ and http://www.matjazperc.com/plos/moral.html

The Emperor’s Dilemma: A Computational Model of Self-Enforcing Norms

The authors demonstrate the uses of agent‐based computational models in an application to a social enigma they call the “emperor’s dilemma,” based on the Hans Christian Andersen fable. In this model, agents must decide whether to comply with and enforce a norm that is supported by a few fanatics and opposed by the vast majority. They find that cascades of self‐reinforcing support for a highly unpopular norm cannot occur in a fully connected social network. However, if agents’ horizons are limited to immediate neighbors, highly unpopular norms can emerge locally and then spread. One might expect these cascades to be more likely as the number of “true believers” increases, and bridge ties are created between otherwise distant actors. Surprisingly, the authors observed quite the opposite effects

Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS

In this paper, we present a geographically explicit agent-based model, loosely coupled with vector GIS, which explicitly captures and uses geometrical data and socio economic attributes in the simulation process. The ability to represent the urban environment as a series of points, line and polygons not only allows one to represent a range of different sized features such as houses or larger areas portrayed as the urban environment but is a move away from many agent-based models utilising GIS which are rooted in grid-based structures. We apply this model to the study of residential segregation, specifically creating a Schelling (1971, 1978) type of model within a hypothetical cityscape, thus demonstrating how this approach can be used for linking vector-based GIS and agent-based modelling. A selection of simulation experiments are presented, highlighting the inner workings of the model and how aggregate patterns of segregation can emerge from the mild tastes and preferences of individual agents interacting locally over time. Furthermore, the paper suggests how this model could be extended and demonstrates the importance of explicit geographical space in the modelling process

Qualitative spatial representation in agent-based models

One of the advantages of agent-based models as simulations of social systems is the ease with which it is possible to spatially embed the agents and their interactions. Spatially explicit representations in agent-based models most typically take the form of raster-based representations in which the space is represented as a grid of squares. More recently, vectorbased representations have been used, usually importing data for the polygons from geographical information systems (GIS). However, for some models, what matters about the space for the purposes of simulation is less the quantitative spatial relationships among entities (e.g. area, distance or direction) than the qualitative relations these quantitative data are used to determine: neighbourhood, and accessibility (which is a general term covering movement and sensing from one region to another). This paper gives consideration to the use of qualitative spatial representations in agent-based modelling, using a model of everyday pro-environmental behaviour in the workplace as an example

Between Replication and Docking: "Adaptive Agents, Political Institutions, and Civic Traditions" Revisited

This article has two primary objectives: (i) to replicate an agent-based model of social interaction by Bhavnani (2003), in which the author explicitly specifies mechanisms underpinning Robert Putnam\'s (1993) work on Civic Traditions in Modern Italy, bridging the gap between the study\'s historical starting point—political regimes that characterized 14th Century Italy—and contemporary levels of social capital—reflected in a \'civic\' North and an \'un-civic\' South; and (ii) to extend the original analysis, using a landscape of Italy that accounts for population density. The replication exercise is performed by different authors using an entirely distinct ABM toolkit (PS-I) with its own rule set governing agent-interaction and cultural change. The extension, which more closely approximates a docking exercise, utilizes equal area cartograms otherwise known as density-equalizing maps (Gastner and Newman 2004) to resize the territory according to 1993 population estimates. Our results indicate that: (i) using the criterion of distributional equivalence, we experience mixed success in replicating the original model given our inability to restrict the selection of partners to \'eligible\' neighbors and limit the number of agent interactions in a timestep; (ii) increasing the number of agents and introducing more realistic population distributions in our extension of the replication model increases distributional equivalence; (iii) using the weaker criteria of relational alignment, both the replication model and its extension capture the basic relationship between institutional effectiveness and civic change, the effect of open boundaries, historical shocks, and path dependence; and (iv) that replication and docking may be usefully combined in model-to-model analysis, with an eye towards verification, reimplementation, and alignment.Replication, Docking, Agent-Based Model, Italy, Social Capital

Errors and Artefacts in Agent-Based Modelling

The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles

Integration of human behavioral aspects in a dynamic model for a manufacturing system

International audienceThe computational simulation of human intelligent behavior has been one of the main research topics in (AI) artificial intelligence domain. Therefore, a great number of behavioral models were proposed considering emotional, cognitive and psychological factors to simulate the human behavior in different domain such as military or manufacturing systems. In addition to psychological factors, the social state of a group of workers plays a critical role in rational decision-making, perception, human interaction and human intelligence. Thus, it is judicious to analyze the workers' behavior at work and to integrate their needs and requirements in manufacturing systems models in order to improve the simulation accuracy. In this context, this paper suggests a graphical and a mathematical representation model of workers' behaviors as well as the ties that can exist among them. The model is also extended to consider inter-worker social relations that can influence the individual performance

Between Monoculture and Cultural Polarization:Agent-based Models of the Interplay of Social Influence and Cultural Diversity

Social influence is one of the most important processes in human social interaction. Very often in human social interaction, influence is assimilative in that individuals become more similar to others they interact with. Nevertheless, cultural differences continue to remain in many realms of human life, for example, in the form of technological boundaries. Research on social influence points to a range of possible reasons for persistent cultural diversity, but there is much less clarity about the interplay of various factors and conditions for cultural influence with fundamental processes of social interaction at the micro-level. In this article, I show how agent-based computational modeling can be used as an approach for unraveling the complex interplay between simple first principles of interpersonal social interaction and emergent societal outcomes. I give a brief overview illustrating some of the main approaches agent-based modelers have developed in recent decades to understand conditions and processes of the emergence of cultural diversity. Models will be discussed that generate mainly cultural consensus as long-term behavior, but also models that generate clustering of cultural attitudes in geographical or social space, and models that imply cultural polarization with sharp cultural boundaries between emergent factions. It will be discussed how model dynamics depend on further assumptions, for example about random events, or the scaling of cultural attitudes, and what are further developments in the literature, possible future directions and challenges for the application of computational agent-based modeling in archeological research on cultural boundaries