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ACACIA-ES: an agent-based modeling and simulation tool for investigating social behaviors in resource-limited two-dimensional environments

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Abstract

In this paper, we describe a framework for studying social agents’ individual decision making, that takes account of the environment and social dynamics. We describe a study in which we explored the efficiency of foraging strategies within a group of individuals faced with a resource-limited environment. We investigated to what extent cooperative and non-cooperative behaviors impacted on the survival rates of a population of individuals. In the experiment presented here, we considered two different types of individuals: selfish individuals who gather energy for their own use, and cooperative individuals who share the energy they gather with others, thus reducing their own individual chances of survival. In order to study the trade-off between non-cooperative and cooperative behaviors in a pseudo-realistic two-dimensional environment, we introduced an agent-based modeling and simulation tool called ACACIA-ES, which simulated local interactions and spatial behavior for large numbers of individuals in complex environments. The main result from our simulation was that a group of cooperative individuals displayed better survival strategies than groups of selfish individuals when faced with a variety of environmental pressures; however, it was very unlikely that such cooperative strategies could resist competition from selfish individuals, if the outcome of past social interactions was memorized, even when a very small group of selfish individuals was introduced.

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Notes

  1. The Gspeed parameter determines resource regrowth according to the following equation: \( {\text{t}}\;\bmod (2 \times 2^{{(6 - {\text{Gspeed}})}} ) = 0, \) in such a way that if a user chooses 2 for the Gspeed parameter, trees will be generated every 32 steps with nf fruits on a randomly chosen free patch within the environment.

  2. An agent's initial energy level is initialized at half the maximal energy it can recharge (\( \varepsilon_{\hbox{max} } \)), where \( \varepsilon_{\hbox{max} } = 200 \) that is: \( \varepsilon_{{{\text{t}} = 0}} \left( {a_{i} } \right) = \frac{1}{2}\varepsilon_{\hbox{max} } = 100. \) This initial energy level for each agent is thus initialized at 100 to allow it to make a tour of the environment perimeter at least once. The energy level \( \varepsilon_{t} (a_{i} ) \) for an agent a i at a time t decreases by one with each step. If the energy level falls to zero, the agent perishes and disappears.

  3. An agent using a “tit-for-tat” (TFT) behavior will initially cooperate, then will respond in the same way that the other agent responded to him.

  4. Preliminary calibration experiments showed this number of replications to provide the best trade-off between statistical relevance and computational cost (not shown here).

  5. For 30 trials in which energy was either shared or not shared, at least 15 % of the population died of starvation after 1000 simulation steps.

  6. Since the possibility of the agents moving obviously depended on the number of obstacles occupying the environment, based on previous results (Zibetti et al. 2007; Salvador et al. 2009), we increased the percentage of vital space slightly (about 84 % of the cells were free) and decreased it slightly (about 89 % of the cells were free). Changes in the percentage of vital space produced respectively a slight increase and a slight decrease in the final agent survival rate in both agent populations but no significant differences were observed between the cooperative and selfish populations after 2000 simulation steps and 200 repetitions.

  7. After 2000 simulation steps, we observed a stabilization in the surviving population and the differences remained static.

  8. For instance, in an ecosystem that was highly limited in terms of available resources (nf = 5 and Gspeed = 3), as well as in a more abundant one (nf = 17 and Gspeed = 5), the reduction in percentage of vital space (from 87 to 84 % free cells) led to a global decrease and increase in agent survivability after 2000 simulation steps in both cooperative and selfish populations respectively, regardless of vital space percentage (MA = 6.57, SDA 2.01; MS = 6.39, SDS 1.80 and MA = 77.36, SDA 5.43; MS = 68.21, SDS 4.94 respectively). These results are comparable to those obtained with the same resource configuration and 87 % free space (Table 2).

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Acknowledgments

We thank Vicenç Quera for helpful discussions for the improving of the computational model. Rob Pratt and Mark Jayes for their help in proofreading. We also thank the two anonymous referees for their useful comments.

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Authors and Affiliations

  1. Laboratoire CHART-LUTIN (Cognition Humaine & Artificielle EA 4004, Université Paris 8, EPHE Paris, Cité des Sciences et de l’Industrie, Paris), Université Paris 8, 2 rue de la liberté, 93526, Saint Denis Cedex 02, France

    Elisabetta Zibetti & Simon Carrignon

  2. Computer Application in Science and Engineering (CASE), Barcelona Supercomputing Center, Edificio NEXUS II, Campus Nord UPC, Gran Capitán 2-4, 08034, Barcelona, Spain

    Simon Carrignon

  3. Sorbonne Universités, UPMC Univ Paris 06, UMR 7222, ISIR, 75005, Paris, France

    Nicolas Bredeche

  4. CNRS, UMR 7222, ISIR, 75005, Paris, France

    Nicolas Bredeche

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  1. Elisabetta Zibetti
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  2. Simon Carrignon
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  3. Nicolas Bredeche
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Correspondence to Elisabetta Zibetti.

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Zibetti, E., Carrignon, S. & Bredeche, N. ACACIA-ES: an agent-based modeling and simulation tool for investigating social behaviors in resource-limited two-dimensional environments. Mind Soc 15, 83–104 (2016). https://doi.org/10.1007/s11299-015-0173-0

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