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Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

Ferretti, Valentina and Geneletti, Davide (2020) Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment. Land Use Policy, 97. ISSN 0264-8377

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Identification Number: 10.1016/j.landusepol.2020.104613

Abstract

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.

Item Type: Article
Additional Information: © 2020 Elsevier Ltd
Divisions: Management
Subjects: H Social Sciences > HD Industries. Land use. Labor
G Geography. Anthropology. Recreation > GE Environmental Sciences
Date Deposited: 30 Jun 2022 10:27
Last Modified: 30 Jun 2022 10:27
URI: http://eprints.lse.ac.uk/id/eprint/115469

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