EVALUATING CONTIGUOUS ALTERNATIVES IN DECISION-MAKING MODELS USING PYTHON PROGRAMMING LANGUAGE
Journal: TERRA POLITICA
№ 1
, 2025
Rubrics: AD HOC
Authors:
1.
The Institute of Europe of the Russian Academy of Sciences
(Department of European Integration Studies, Research Fellow)
Moscow, Russian Federation
Moscow, Russian Federation
Type:
Article
Pages:
from 157
to 166
Status:
Published
Received:
01.03.2025
Accepted:
01.06.2025
Published:
01.07.2025
Subject area:
VAK Russia 1.6.13
Language:
Russian
Keywords:
agent-based modeling, contiguity matrix, game theory, optimization methods, preferences
Abstract (English):
Python programming language allows for storing and processing information about neighboring geographic objects compared by political actors. The most suitable open-source recent libraries for analyzing and predicting movements along a coordinate plane or adjacency graph (for example, in rational choice studies, diffusion of innovations and shaping institutions or relocation to an already reshaped institutional environment) are presumably Helipad and Mesa.;Actors’ «digital twins» access information about their neighborhoods (encoded explicitly or derived from a polygon’s coordinates) when interacting with the environment: by limiting the number of alternatives for displacement or allocation at the next moment (during a step-by-step simulation) to a list of nearby places.;When choosing the optimal location, computer models compare the relevant characteristics of neighboring objects, focusing on encoded preferences. To that end an actor automatically calculates the utility it would have received by the end of the period thanks to the envisaged zone’s properties, and the most acceptable nearby alternative is determined. Some libraries contain ready-made procedures visualizing the territories and the space they form (as a heat map - with areas’ colors depending on the value of any parameter). By reformulating the research task (by shifting the emphasis from the territories’ characteristics to the properties of moving in various directions), one could use libraries based on game theory. Algorithms to compare strategies (relocating to neighboring areas) are provided in libraries Axelrod, QuantEcon, StratPy, NashPy, OpenSpiel.
Python programming language allows for storing and processing information about neighboring geographic objects compared by political actors. The most suitable open-source recent libraries for analyzing and predicting movements along a coordinate plane or adjacency graph (for example, in rational choice studies, diffusion of innovations and shaping institutions or relocation to an already reshaped institutional environment) are presumably Helipad and Mesa.;Actors’ «digital twins» access information about their neighborhoods (encoded explicitly or derived from a polygon’s coordinates) when interacting with the environment: by limiting the number of alternatives for displacement or allocation at the next moment (during a step-by-step simulation) to a list of nearby places.;When choosing the optimal location, computer models compare the relevant characteristics of neighboring objects, focusing on encoded preferences. To that end an actor automatically calculates the utility it would have received by the end of the period thanks to the envisaged zone’s properties, and the most acceptable nearby alternative is determined. Some libraries contain ready-made procedures visualizing the territories and the space they form (as a heat map - with areas’ colors depending on the value of any parameter). By reformulating the research task (by shifting the emphasis from the territories’ characteristics to the properties of moving in various directions), one could use libraries based on game theory. Algorithms to compare strategies (relocating to neighboring areas) are provided in libraries Axelrod, QuantEcon, StratPy, NashPy, OpenSpiel.
Keywords:
agent-based modeling, contiguity matrix, game theory, optimization methods, preferences
agent-based modeling, contiguity matrix, game theory, optimization methods, preferences
1. Axelrod R. (1997), The complexity of cooperation: Agent-based models of competition and collaboration, Princeton: Princeton University Press, 248 p. DOI: https://doi.org/10.1515/9781400822300
2. Zhang J. (2011), Tipping and residential segregation: A unified schelling model, Journal of Regional Science, vol. 51, no. 1, pp. 167-193.
