Declan Mungovan

National University of Ireland, Galway, Gaillimh, Connaught, Ireland

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Publications (3)0.92 Total impact

  • Declan Mungovan, Enda Howley, Jim Duggan
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    ABSTRACT: In this paper we explore the effect that random social interactions have on the emergence and evolution of social norms in a simulated population of agents. In our model agents observe the behaviour of others and update their norms based on these observations. An agent’s norm is influenced by both their own fixed social network plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual’s path distance on the network to determine their chance of meeting a stranger. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. We then contrast the cases where agents make highest utility based rational decisions about which norm to adopt versus using a Markov Decision process that associates a weight with the best choice. Finally we examine the effect that these random interactions have on the evolution of a more complex social norm as it propagates throughout the population. We discover that increasing the frequency and weighting of random interactions results in higher levels of norm convergence and in a quicker time when agents have the choice between two competing alternatives. This can be attributed to more information passing through the population thereby allowing for quicker convergence. When the norm is allowed to evolve we observe both global consensus formation and group splintering depending on the cognitive agent model used.
    Computational and Mathematical Organization Theory 05/2011; 17:152-178. DOI:10.1007/s10588-011-9085-7 · 0.92 Impact Factor
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    Declan Mungovan, Enda Howley, Jim Duggan
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    ABSTRACT: Agent Based Modelling (ABM) is a methodology used to study the behaviour of norms in complex systems. Agent based sim- ulations are capable of generating populations of heterogeneous, self- interested agents that interact with one another. Emergent norm be- haviour in the system may then be understood as a result of these in- dividual interactions. Agents observe the behaviour of their group and update their belief based on those of others. Social networks have been shown to play an important role in norm convergence. In this model 1 agents interact on a flxed social network with members of their own social group plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted se- lection algorithm that uses an individual's path distance on the network. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. Using this method we investigate the efiect that random interactions have on the dissemination of social norms when agents are primarily in∞uenced by their social network. We discover that increasing the frequency and quality of random interactions results in an increase in the rate of norm convergence.
    Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Dublin, Ireland, August 19-21, 2009, Revised Selected Papers; 01/2009
  • Source
    Declan Mungovan, Enda Howley, Jim Duggan
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    ABSTRACT: Computer viruses pose a signiflcant security threat to orga- nizations and computer users today. Viruses can propagate rapidly across networks through emails that are sent from user to user, such networks have been shown to possess a scale free1 conflguration. This paper2 inves- tigates the outcome of allowing nodes on a scale free network to choose their own level of antivirus defence. There is a cost associated with de- fending against viruses and a loss incurred if a node becomes infected. A multi-agent model is presented where each node, using independent strategies, can decide to increase or reduce their level of antivirus de- fence depending on their own virus experience on the network. Our ex- periments indicate that if nodes are allowed to set their own level of antivirus defence based on their infection level, then most of the network will become infected. If, however, a node biases its behaviour based on its connectivity, the level of infection shows a marked reduction.

Publication Stats

4 Citations
0.92 Total Impact Points

Institutions

  • 2009–2011
    • National University of Ireland, Galway
      • Department of Information Technology
      Gaillimh, Connaught, Ireland