Repeated discrete choices in geographical agent based models with an application to fisheries

Authored by Ernesto Carrella, Richard M Bailey, Jens Koed Madsen

Date Published: 2019

DOI: 10.1016/j.envsoft.2018.08.023

Sponsors: No sponsors listed

Platforms: Java MASON

Model Documentation: ODD Flow charts Pseudocode

Model Code URLs: https://github.com/CarrKnight/discrete-choosers https://github.com/CarrKnight/POSEIDON

Abstract

Most geographical agent-based models simulate agents through custom-made decision-making algorithms. This makes it difficult to assess which results are general and which are contingent on the algorithm's details. We present a set of general algorithms, applicable in any agent-based model for choosing repeatedly from a set of alternatives. We showcase each in the same fishery agent-based model and rank their performance under various scenarios. While complicated algorithms tend to perform better, too much sophistication lowers performance. Further, while some algorithms perform well under all scenarios, others are optimal only in specific circumstances. It is therefore impossible to produce a single, unequivocal performance ranking even for simple general algorithms. We advocate then a heuristic zoo approach where multiple algorithms are implemented in the same model; this allows us to identify its best algorithm and test sensitivity to misspecifications of the decision-making component.

Tags

Adaptation Simulation Agent-based models behavior Dynamics Decision-Making Search information fisheries Convergence location choice Strategies Multi-armed bandit Bio-economic modelling Weighted regression