17,910 research outputs found

    Micro Behavioural Attitudes and Macro Technological Adaptation in Industrial Districts. An Agent-Based Prototype

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    Industrial Districts (IDs) are complex productive systems based on an evolutionary network of heterogeneous, functionally integrated and complementary firms, which are within the same market and geographical space. Setting up a prototype, able to reproduce an idealised ID, we model cognitive processes underlying the behaviour of ID firms. ID firms are bounded rationality agents, able to process information coming from technology and market environment and from their relational contexts. They are able to evaluate such information and to transform it into courses of action, routinising their choices, monitoring the environment, categorising, typifying and comparing information. But they have bounded cognitive resources: attention, time and memory. We test two different settings: the first one shows ID firms behaving according to a self-centred attitude, while the second one shows ID firms behaving according to a social centred attitude. We study how such a strong difference at micro-level can affect at macro-level the technological adaptation of IDs

    Modelling Fresh Strawberry Supply "From-Farm-to-Fork" as a Complex Adaptive Network

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     The purpose of this study is to model and thereby enable simulation of the complete business entity of fresh food supply. A case narrative of fresh strawberry supply provides basis for this modelling. Lamming et al. (2000) point to the importance of discerning industry-specific product features (or particularities) regarding managing supply networks when discussing elements in "an initial classification of a supply network" while Fisher (1997) and Christopher et al. (2006, 2009) point to the lack of adopting SCM models to variations in products and market types as an important source of SCM failure. In this study we have chosen to move along a research path towards developing an adapted approach to model end-to-end fresh food supply influenced by a combination of SCM, system dynamics and complex adaptive network thinking...

    Simulating the Effect of Social Influence on Decision-Making in Small, Task-Oriented, Groups

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    This paper describes a simulation study of decision-making. It is based on a model of social influence in small, task-oriented, groups. A process model of dyadic social influence is built on top of a dynamic model of status and task participation that describes the emergence of a stable power and prestige order. Two models of group decision-making are examined: a static model for which the beliefs of actors do not change, and a process model for which they do as a function of the standing of each member of each interacting pair in the evolving power and prestige order. The models are compared on a set of N=111 cases, each requiring an affirmative or negative group response to a proposition A(c) that pertains to a case c. Initial beliefs are assigned to each of five members of distinct professions based on an analysis of independently collected behavioral data pertinent to the proposition to be affirmed or denied in each case. Although the two influence models yield identical decisions in 70% of the cases examined, the differences between them are statistically significant and in several instances show a medium effect size. Most importantly, the differences can be explained in terms of social influence and the status and task participation model on which it depends.Social Influence; Decision Processes; Social Networks; Group Dynamics; Simulation; Agent-Based Modeling

    Social Mental Shaping: Modelling the Impact of Sociality on Autonomous Agents' Mental States

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    This paper presents a framework that captures how the social nature of agents that are situated in a multi-agent environment impacts upon their individual mental states. Roles and relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief-Desire-Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem-solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent's mental state

    Contribution of simulation and gaming to natural resource management issues: An introduction

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    Nowadays, computer-mediated simulations and games are widely used in the field of natural resource management (NRM). They have proved to be useful for various purposes such as supporting decisionmaking processes and training. First, the specificities of the NRM research field are highlighted. Then, based on the analysis of the articles presented in this special issue of Simulation & Gaming, some key features related to the implementation of gaming in such a context are introduced. Finally, after reviewing the benefits of using simulation games in NRM, the authors stress the ethical issue of changing social relationships among stakeholders by playing a game with some of themGESTION DE L'ENVIRONNEMENT;RESSOURCE NATURELLE;SIMULATION;SOCIOLOGIE;JEU DE ROLE;BENEFITS;CONTEXT;COLLECTIVE POLICY DESIGN;DECISION MAKING;ETHICAL ISSUES;IMPLEMENTATION;NATURAL RESOURCE MANAGEMENT (NRM);SIMULATION GAMES;SOCIAL EMPOWERMENT;SOCIAL RELATIONSHIPS;SOCIOECOLOGICAL SYSTEMS;STAKEHOLDERS

    On Agent-Based Software Engineering

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    Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures

    Social capital, transition in agriculture, and economic organisation: a theoretical perspective

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    Social capital is defined as the shared knowledge, trust, and culture, embodied in the structural forms of networks and other stable inter-agent relationships. Social capital has been shown to be more difficult to build than economic capital, and to have greater beneficial effects for community as a whole. The relevance of the social capital concept for transitional agenda is explained by the increasing responsibility of private collective action and grass-roots decisions in managing the business activities in agriculture, since this is required by the more democratic foundations of the market economy. Different forms of business organisations are shown to be differentially but consistently associated with social capital, with the major social capital dependent organisational form being the cooperative. The growing complexity of inter-agent relations (particularly in transitional context) causes the increasing amount of economic responsibility being transferred from authority-based to social capital-based forms of economic organisation, i.e. from markets and hierarchies, based mainly on economic capital, to networks with their primary accent on social capital. The social capital-based organisation in agriculture is particularly important in view of its industry-specific limitations and is represented mainly by cooperatives and farmers associations. The optimal role of the government is to invest in social capital in order to enable rural communities to solve their problems by means of private collective action (self-organisation), rather than attempt to substitute the latter. -- G E R M A N V E R S I O N: Sozialkapital wird definiert als geteiltes Wissen, Vertrauen und gemeinsame Kultur, eingebettet in Netzwerkstrukturen und andere stabile Beziehungen zwischen Agenten. Es hat sich gezeigt, dass Sozialkapital schwieriger aufzubauen ist als ökonomisches Kapital und dass es größere Auswirkungen auf die Gemeinschaft als Ganzes hat. Die Relevanz des Sozialkapital-Konzeptes für die Agenda der Transformationsländer wird erklärt durch die wachsende Verantwortung von privaten, kollektiven Handlungen und Basisentscheidungen beim landwirtschaftlichen Betriebsmanagement, wie es für die demokratischen Strukturen der Marktwirtschaft erforderlich ist. Verschiedene Betriebsformen sind unterschiedlich, jedoch durchweg verbunden mit Sozialkapital. Die landwirtschaftlichen Produktionsgenossen-schaften erweisen sich dabei als am meisten abhängig von Sozialkapital. Die wachsende Komplexität der Inter-Agenten-Beziehungen (insbesondere im Kontext des Transformationsprozesses) bewirkt, dass ein steigender Anteil ökonomischer Verantwortung von autoritätsbasierten zu sozialkapital-basierten Organisationsformen übergeht, d. h. von Märkten und Hierarchien, die vor allem auf ökonomischen Kapital basieren, zu Netzwerken mit dem Schwerpunkt auf Sozialkapital. Die sozialkapitalbasierten Organisationen in der Landwirtschaft werden hauptsächlich durch Genossenschaften und Bauernverbände repräsentiert und sind besonders wichtig in Hinblick auf ihre industriespezifischen Beschränkungen. Politische Maßnahmen sollten Investitionen in Sozialkapital unterstützen, um ländliche Gemeinden zu befähigen, ihre Probleme durch private, kollektive Handlungen (Selbstorganisation), anstatt zu versuchen, diese zu ersetzen.social capital,agricultural cooperative,economic organisation,Sozialkapital,Agrargenossenschaft,ökonomische Organisation

    Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

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    The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models

    Towards formal models and languages for verifiable Multi-Robot Systems

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    Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin
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