Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137740
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Adamson, D. | - |
dc.contributor.author | Loch, A. | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Water Economics and Policy, 2022; 8(4):2240011-2240011 | - |
dc.identifier.issn | 2382-624X | - |
dc.identifier.issn | 2382-6258 | - |
dc.identifier.uri | https://hdl.handle.net/2440/137740 | - |
dc.description | Published: 20 February 2023 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | David Adamson and Adam Loch | - |
dc.language.iso | en | - |
dc.publisher | World Scientific Publishing Company | - |
dc.rights | © World Scientific Publishing Company | - |
dc.source.uri | http://dx.doi.org/10.1142/s2382624x22400112 | - |
dc.subject | State contingent; robustness tests; decision-making; water; risk | - |
dc.title | Overcoming deterministic limits to robustness tests of decision-making given incomplete information: the state contingent analysis approach | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1142/S2382624X22400112 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE160100213 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE150100328 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Adamson, D. [0000-0003-1616-968X] | - |
dc.identifier.orcid | Loch, A. [0000-0002-1436-8768] | - |
Appears in Collections: | Global Food Studies publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_137740.pdf | Accepted version | 1.23 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.