Overcoming Deterministic Limits to Robustness Tests of Decision-Making Given Incomplete Information: The State Contingent Analysis Approach
Adamson, David and Loch, Adam (2023) Overcoming Deterministic Limits to Robustness Tests of Decision-Making Given Incomplete Information: The State Contingent Analysis Approach. Water Economics and Policy, 48 (4). 2240011(20220.
|
Text
Adamson & Loch 2023.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Keywords: | State contingent, robustness tests, decision-making, water, risk |
| Divisions: | Agriculture, Science and Practice |
| Depositing User: | Dr David Adamson |
| Date Deposited: | 08 Nov 2024 10:29 |
| Last Modified: | 08 Nov 2024 10:29 |
| URI: | https://rau.repository.guildhe.ac.uk/id/eprint/16800 |
Actions (login required)
 |
Edit Item |