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Is the person-situation debate important for agent-based modeling and vice-versa?

Sznajd-Weron K, Szwabiński J, Weron R - PLoS ONE (2014)

Bottom Line: Studying two variants of the same agent-based model of opinion formation, we show that the decision to choose either personal traits or the situation as the primary factor driving social interactions is of critical importance.Using Monte Carlo simulations (for Barabasi-Albert networks) and analytic calculations (for a complete graph) we provide evidence that assuming a person-specific response to social influence at the microscopic level generally leads to a completely different and less realistic aggregate or macroscopic behavior than an assumption of a situation-specific response; a result that has been reported by social psychologists for a range of experimental setups, but has been downplayed or ignored in the opinion dynamics literature.This sensitivity to modeling assumptions has far reaching consequences also beyond opinion dynamics, since agent-based models are becoming a popular tool among economists and policy makers and are often used as substitutes of real social experiments.

View Article: PubMed Central - PubMed

Affiliation: Institute of Physics, Wrocław University of Technology, Wrocław, Poland.

ABSTRACT

Background: Agent-based models (ABM) are believed to be a very powerful tool in the social sciences, sometimes even treated as a substitute for social experiments. When building an ABM we have to define the agents and the rules governing the artificial society. Given the complexity and our limited understanding of the human nature, we face the problem of assuming that either personal traits, the situation or both have impact on the social behavior of agents. However, as the long-standing person-situation debate in psychology shows, there is no consensus as to the underlying psychological mechanism and the important question that arises is whether the modeling assumptions we make will have a substantial influence on the simulated behavior of the system as a whole or not.

Methodology/principal findings: Studying two variants of the same agent-based model of opinion formation, we show that the decision to choose either personal traits or the situation as the primary factor driving social interactions is of critical importance. Using Monte Carlo simulations (for Barabasi-Albert networks) and analytic calculations (for a complete graph) we provide evidence that assuming a person-specific response to social influence at the microscopic level generally leads to a completely different and less realistic aggregate or macroscopic behavior than an assumption of a situation-specific response; a result that has been reported by social psychologists for a range of experimental setups, but has been downplayed or ignored in the opinion dynamics literature.

Significance: This sensitivity to modeling assumptions has far reaching consequences also beyond opinion dynamics, since agent-based models are becoming a popular tool among economists and policy makers and are often used as substitutes of real social experiments.

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Related in: MedlinePlus

Concentration of adopted c in the stationary state as a function of independence p for the person (left column) and the situation (right column) models.Simulation results are averaged over 1000 Monte Carlo runs and concern Barabasi-Albert networks of size . In the top row the dependence on flexibility f is shown for , in the bottom row the dependence on M is shown for . Note that the results for larger values of M approach the results for the CG, see Fig. 3.
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pone-0112203-g002: Concentration of adopted c in the stationary state as a function of independence p for the person (left column) and the situation (right column) models.Simulation results are averaged over 1000 Monte Carlo runs and concern Barabasi-Albert networks of size . In the top row the dependence on flexibility f is shown for , in the bottom row the dependence on M is shown for . Note that the results for larger values of M approach the results for the CG, see Fig. 3.

Mentions: We investigate both modeling approaches on Barabasi-Albert networks, as they nicely recover most of the features of a real social network [39]. We build the test network starting from a fully connected graph of M nodes and then preferentially attach M new nodes at each time step until the network achieves the assumed number of nodes N. We then conduct Monte Carlo simulations. In the initial state all spinsons are 'down', which corresponds to the situation prior to introducing the innovation (e.g. a tablet, a new electricity tariff) when none of the agents is 'adopted'. Due to independence some spinsons start to flip and then social influence from a unanimous group of spins may influence a neighboring (and connected) spinson. Eventually the system reaches a stationary state in which concentration of adopted fluctuates around some average value . As a result of competition between social influence (an ordering force) and independence (which introduces noise and disorders the system), a phase transition appears in both models (see Fig. 2). For level of independence there is a state in which a majority () coexists with a minority and for a status-quo situation is observed (). Surprisingly, in the person model there is no dependence on parameter f, which describes how often independent spinsons change their opinion. On the other hand, f influences the results significantly in the situation model (see the top right panel in Fig. 2).


Is the person-situation debate important for agent-based modeling and vice-versa?

Sznajd-Weron K, Szwabiński J, Weron R - PLoS ONE (2014)

Concentration of adopted c in the stationary state as a function of independence p for the person (left column) and the situation (right column) models.Simulation results are averaged over 1000 Monte Carlo runs and concern Barabasi-Albert networks of size . In the top row the dependence on flexibility f is shown for , in the bottom row the dependence on M is shown for . Note that the results for larger values of M approach the results for the CG, see Fig. 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219838&req=5

pone-0112203-g002: Concentration of adopted c in the stationary state as a function of independence p for the person (left column) and the situation (right column) models.Simulation results are averaged over 1000 Monte Carlo runs and concern Barabasi-Albert networks of size . In the top row the dependence on flexibility f is shown for , in the bottom row the dependence on M is shown for . Note that the results for larger values of M approach the results for the CG, see Fig. 3.
Mentions: We investigate both modeling approaches on Barabasi-Albert networks, as they nicely recover most of the features of a real social network [39]. We build the test network starting from a fully connected graph of M nodes and then preferentially attach M new nodes at each time step until the network achieves the assumed number of nodes N. We then conduct Monte Carlo simulations. In the initial state all spinsons are 'down', which corresponds to the situation prior to introducing the innovation (e.g. a tablet, a new electricity tariff) when none of the agents is 'adopted'. Due to independence some spinsons start to flip and then social influence from a unanimous group of spins may influence a neighboring (and connected) spinson. Eventually the system reaches a stationary state in which concentration of adopted fluctuates around some average value . As a result of competition between social influence (an ordering force) and independence (which introduces noise and disorders the system), a phase transition appears in both models (see Fig. 2). For level of independence there is a state in which a majority () coexists with a minority and for a status-quo situation is observed (). Surprisingly, in the person model there is no dependence on parameter f, which describes how often independent spinsons change their opinion. On the other hand, f influences the results significantly in the situation model (see the top right panel in Fig. 2).

Bottom Line: Studying two variants of the same agent-based model of opinion formation, we show that the decision to choose either personal traits or the situation as the primary factor driving social interactions is of critical importance.Using Monte Carlo simulations (for Barabasi-Albert networks) and analytic calculations (for a complete graph) we provide evidence that assuming a person-specific response to social influence at the microscopic level generally leads to a completely different and less realistic aggregate or macroscopic behavior than an assumption of a situation-specific response; a result that has been reported by social psychologists for a range of experimental setups, but has been downplayed or ignored in the opinion dynamics literature.This sensitivity to modeling assumptions has far reaching consequences also beyond opinion dynamics, since agent-based models are becoming a popular tool among economists and policy makers and are often used as substitutes of real social experiments.

View Article: PubMed Central - PubMed

Affiliation: Institute of Physics, Wrocław University of Technology, Wrocław, Poland.

ABSTRACT

Background: Agent-based models (ABM) are believed to be a very powerful tool in the social sciences, sometimes even treated as a substitute for social experiments. When building an ABM we have to define the agents and the rules governing the artificial society. Given the complexity and our limited understanding of the human nature, we face the problem of assuming that either personal traits, the situation or both have impact on the social behavior of agents. However, as the long-standing person-situation debate in psychology shows, there is no consensus as to the underlying psychological mechanism and the important question that arises is whether the modeling assumptions we make will have a substantial influence on the simulated behavior of the system as a whole or not.

Methodology/principal findings: Studying two variants of the same agent-based model of opinion formation, we show that the decision to choose either personal traits or the situation as the primary factor driving social interactions is of critical importance. Using Monte Carlo simulations (for Barabasi-Albert networks) and analytic calculations (for a complete graph) we provide evidence that assuming a person-specific response to social influence at the microscopic level generally leads to a completely different and less realistic aggregate or macroscopic behavior than an assumption of a situation-specific response; a result that has been reported by social psychologists for a range of experimental setups, but has been downplayed or ignored in the opinion dynamics literature.

Significance: This sensitivity to modeling assumptions has far reaching consequences also beyond opinion dynamics, since agent-based models are becoming a popular tool among economists and policy makers and are often used as substitutes of real social experiments.

Show MeSH
Related in: MedlinePlus