Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137740
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Type: | Journal article |
Title: | Overcoming deterministic limits to robustness tests of decision-making given incomplete information: the state contingent analysis approach |
Author: | Adamson, D. Loch, A. |
Citation: | Water Economics and Policy, 2022; 8(4):2240011-2240011 |
Publisher: | World Scientific Publishing Company |
Issue Date: | 2022 |
ISSN: | 2382-624X 2382-6258 |
Statement of Responsibility: | David Adamson and Adam Loch |
Abstract: | Incomplete information may result in multiple factors combining to jointly affect the consequences of decision-making. The typical response to incomplete information has been tests of robustness and a fixed decisions’ capacity to withstand a wide variety of future conditions. But what of reversed contexts, where the revealed future alters decision-making via experience, learning and innovation such that the decision itself changes? In this paper we contrast a commonly applied expected value robustness metric to state contingent analysis which allows for learning and innovation. State contingent analysis views robustness as how decision-makers achieve profits across all future states by reallocating resources ex post to maximize payoffs and/or minimize losses via outputs that are conditionally specific. Consequently, the state-contingent approach enables researchers to identify the benefits and constraints of resource reallocation—rather than fixed decision-making—over plausible scenarios. Within SCA, scenarios can thus be uncoupled from the historical averages to explore rare events, even if never before experienced, including thin- and fat-tailed probability distribution outcomes and their impact on decision-making, innovation and future solutions. A case study assessment of water resource management in a large river basin provides the basis for our comparison. We find that expected value models mask innovation and adaptation reactions by decision-makers in response to external stimuli (e.g., increased droughts) and under-represent water reallocation outcomes. Conversely, state contingent models represent and report decision-maker reactions that can be more readily interpreted and linked to stimuli including policy interventions, expanding the study of complex human-water systems. |
Keywords: | State contingent; robustness tests; decision-making; water; risk |
Description: | Published: 20 February 2023 |
Rights: | © World Scientific Publishing Company |
DOI: | 10.1142/S2382624X22400112 |
Grant ID: | http://purl.org/au-research/grants/arc/DE160100213 http://purl.org/au-research/grants/arc/DE150100328 |
Published version: | http://dx.doi.org/10.1142/s2382624x22400112 |
Appears in Collections: | Global Food Studies publications |
Files in This Item:
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hdl_137740.pdf | Accepted version | 1.23 MB | Adobe PDF | View/Open |
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