Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.634491
Title: Beyond lucky : measuring and modelling the impact of 'probability control' on risky choice
Author: Agarwal, Shweta
ISNI:       0000 0004 5351 4961
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2014
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Abstract:
Managers frequently deal with risk by considering uncertainty as an element of the decision problem over which they can exert control — for example, lobbyists trying to exert influence over regulators or managers trying to mitigate Operational Risks related to human processes. This perspective that the probabilities of uncertain events are at times ‘mutable’ — i.e. subject to one’s influence — has an important and previously under-appreciated role in decision-making under risk. The present research, structured as a series of three papers, addresses this gap between theory and practice on the topic of ‘control’ from a descriptive, theoretical and prescriptive perspective. The descriptive paper discusses a novel empirical test of the behavioural effect of ‘control’ on risk taking. The key finding that control does not always enhance risk taking but, instead, has a moderating effect on attitudes to risk, extends insights from related research. Strong preference for exerting control to eliminate uncertainty is also revealed. Affective and cognitive interpretations of the findings are offered and their correspondence with managerial attitudes to risk taking is discussed. The theoretical paper builds on methods in Decision Analysis and Philosophy, and develops a new probability revision rule for modelling control as interventions on uncertainties. This rule is shown to dramatically alleviate the judgmental burden of analysing multiple interventions. Foundational properties for probability revision rules for interventions, similar to the coherence criterion for Bayes rule, are also constructed and a proof that the proposed rule satisfies these properties is offered. In the prescriptive paper, a real world application of the probability revision rule is illustrated in the context of Operational Risk assessment, where several uncertainties are controllable (e.g. staff strikes). It is shown how this rule can be integrated with Operational Risk calculations to explicitly incorporate the effect of managerial mitigations on loss events, thus making a useful contribution to the field. In summary, this research explores the concept of ‘probability control’ as a way to manage risks in the context of Decision Sciences. It furthers our behavioural understanding of risk attitudes to better resonate with managerial perspectives on risk taking and extends the relevance of Decision Analysis methods to corporate risk management.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.634491  DOI: Not available
Keywords: HD28 Management. Industrial Management
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