Understanding social behavior evolutions through agent-based modeling

Nemiche, Mohamed; Cavero, V.; López, Rafael Pla

doi:10.1109/icmcs.2012.6320322

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…When an agent occupies the cell (x,y), the value K ( x , y ) is updated as (Nemiche et al ., ):

K^{t + 1} (x, y) = \min [K^{t} (x, y) + ρ . K^{t} (x, y) - (C S^{t} + C R^{t}), K_{italicmax}]

where CS and CR are the consumption of the resources in satisfaction and repression by the agent, respectively.…”

Section: Description Of the Modelmentioning

confidence: 99%

“…Which guarantees that RC ( U ) = 0 if μ ( U ) = 0 or ν ( U ) = 0 The might : we think that the might of a social behavior U of an agent A increases with the dimension of U and with the included attributes; in a way that a social behavior with a great dimension possessus a great might. Taking into consideration the binary representation of social behavior, the simple function that satisfies these conditions is the decimal representation of the social behavior U (Nemiche and Pla‐López, ) ; that is, μ ( U ) = ∑ i 2 i U i . The ferocity : we assume that the ferocity decreases in the social behaviours that are more advanced (social behavior U with U mmax‐1 = 1), and increases in the social behaviours that are less advanced ( U mmax‐1 = 0).The formula of the ferocity in this model is (Nemiche et al ., ) (Figure ):

ν (U) = 1 - {((), \frac{2. true \sum_{i} 2^{i} U_{i}}{2^{m_{max}} - 1} - 1)}^{2}

We want the initial repressive capacity of the social behavior (0,1,…,1,1) to be equal to 1. For this, we have to divide the might by 2 mmax‐1 − 1 .…”

Section: Social Behavior Proprietiesmentioning

confidence: 99%

“…The social neighbour of the agent A at the instant t , according to this version, is the set

V_{A}^{t}

of agents that socially impact/affect the agent A (Nemiche et al ., ):

V_{A}^{t} = \{Agent B / IM P^{t} (B, A) \neq 0 and B \neq A\}

…”

Section: Social Neighbouringmentioning

confidence: 99%

“…where CS and CR are the consumption of the resources in satisfaction and repression by the agent, respectively. In this model, a cell (x,y) only can be occupied by one agent at the instant t. When the cell Figure 1 Representation of the environment in two-dimensional is free, the value of K(x,y) increases under the following formula (Nemiche et al, 2012):…”

Section: Environmentmentioning

confidence: 99%

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A Theoretical Agent‐Based Model to simulate an Artificial Social Evolution

et al. 2013

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Agent‐based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to build a theoretical agent‐based model to simulate the social evolution of a set of agents/artificial societies. In this model, each agent (artificial society) has an available number of social behaviours that compete among each other. The agent/agent interactions are carried out by their social otherwise, the agent/environment interactions are expressed through the consumption of ecological resources in repression and satisfaction by social behaviours of the agent. In this work, we will present the structure and the formulation of our model. This model can capture emergent social phenomena such as globalization and polarization, and it can help to understand the social migration process. Copyright © 2013 John Wiley & Sons, Ltd.

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“…When an agent occupies the cell (x,y), the value K ( x , y ) is updated as (Nemiche et al ., ):

K^{t + 1} (x, y) = \min [K^{t} (x, y) + ρ . K^{t} (x, y) - (C S^{t} + C R^{t}), K_{italicmax}]

where CS and CR are the consumption of the resources in satisfaction and repression by the agent, respectively.…”

Section: Description Of the Modelmentioning

confidence: 99%

ν (U) = 1 - {((), \frac{2. true \sum_{i} 2^{i} U_{i}}{2^{m_{max}} - 1} - 1)}^{2}

We want the initial repressive capacity of the social behavior (0,1,…,1,1) to be equal to 1. For this, we have to divide the might by 2 mmax‐1 − 1 .…”

Section: Social Behavior Proprietiesmentioning

confidence: 99%

“…The social neighbour of the agent A at the instant t , according to this version, is the set

V_{A}^{t}

of agents that socially impact/affect the agent A (Nemiche et al ., ):

V_{A}^{t} = \{Agent B / IM P^{t} (B, A) \neq 0 and B \neq A\}

…”

Section: Social Neighbouringmentioning

confidence: 99%

Section: Environmentmentioning

confidence: 99%

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A Theoretical Agent‐Based Model to simulate an Artificial Social Evolution

et al. 2013

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“…Utility models emphasize the psychological and economic considerations along the decision making process, while heuristic models address how individuals make decisions under the influence of social connections. When modeling collective actions in cyber space, existed works pay more attention to the interact mechanism between agents (Nemiche, 2012), the impact of information content to collective behaviors (Margetts, 2012), or the control of trust during social network evolution (Taddei, 2013). Those research works focus on the influence of social networks.…”

Section: Introductionmentioning

confidence: 99%

A Social Choice Force Model for Estimating Collective Action Manipulation

Zhang

Zeng

Liu

et al. 2014

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Modelling “Occident/Orient” duality and migration process with mobile agents

et al. 2019

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The history of the evolution of human societies up to the present day has been marked by large‐scale collective migrations as well as by a clear duality between a West dominated by individualistic social behaviours and an East dominated by collective social behaviours. Only, at the end of the 20th century, the two lines of evolution converge towards a capitalist globalization.The main objective of this work is to build an agent‐based model from the mathematical model of Rafael Pla‐López in order to simulate the social evolution of an artificial society. The simulations of our model make it possible to understand the collective migration process and different lines of social evolution such as Occident/Orient duality. This model can serve as a virtual laboratory to understand the processes involved in human social evolution and explore different trajectories of human social evolution.

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Understanding social behavior evolutions through agent-based modeling

Cited by 6 publications

References 8 publications

A Theoretical Agent‐Based Model to simulate an Artificial Social Evolution

A Theoretical Agent‐Based Model to simulate an Artificial Social Evolution

A Social Choice Force Model for Estimating Collective Action Manipulation

Modelling “Occident/Orient” duality and migration process with mobile agents

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