A Massively Multi-user Online Game Framework for Agent-Based Spatial Simulation

Author(s):  
David Massey ◽  
Ola Ahlqvist ◽  
Kiril Vatev ◽  
Johnathan Rush
Author(s):  
Henk Elffers ◽  
Pieter Van Baal

This chapter considers whether it is worthwhile and useful to enrich agent based spatial simulation studies in criminology with a real geographical background, such as the map of a real city? Using modern GIS tools, such an enterprise is in principle quite feasible, but we argue that in many cases this course is not only not producing more interesting results, but in fact may well be detrimental for the real reason of doing criminal simulation studies, which is understanding the underlying rules. The argument is first outlined in general, and then illustrated in the context of a given example of the ThESE perceptual deterrence simulation model (Van Baal, 2004), a model that actually is using a simple checkerboard as its spatial backcloth.


2008 ◽  
Vol 32 (6) ◽  
pp. 417-430 ◽  
Author(s):  
Andrew Crooks ◽  
Christian Castle ◽  
Michael Batty

2019 ◽  
Vol 16 (156) ◽  
pp. 20180814 ◽  
Author(s):  
Kunal Bhattacharya ◽  
Tuomas Takko ◽  
Daniel Monsivais ◽  
Kimmo Kaski

As a step towards studying human-agent collectives, we conduct an online game with human participants cooperating on a network. The game is presented in the context of achieving group formation through local coordination. The players set initially to a small-world network with limited information on the location of other players, coordinate their movements to arrange themselves into groups. To understand the decision-making process, we construct a data-driven model of agents based on probability matching. The model allows us to gather insight into the nature and degree of rationality employed by the human players. By varying the parameters in agent-based simulations, we are able to benchmark the human behaviour. We observe that while the players use the neighbourhood information in limited capacity, the perception of risk is optimal. We also find that for certain parameter ranges, the agents are able to act more efficiently when compared to the human players. This approach would allow us to simulate the collective dynamics in games with agents having varying strategies playing alongside human proxies.


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