Regulating Industries under Exogenous Uncertainty

Evatt, G.W. and Moriarty, J. and Johnson, P.V and Duck, P.W. (2011) Regulating Industries under Exogenous Uncertainty. [MIMS Preprint]

This is the latest version of this item.

[thumbnail of MIMS_ep2011_73.pdf] PDF
MIMS_ep2011_73.pdf

Download (243kB)

Abstract

We present a quantitative method to find jointly optimal strategies for an industry regulator and a firm, who operate under exogenous uncertainty. The firm controls its operating policy in order to maximize its expected future profits, whilst taking account of regulatory fines. The regulator aims to control the probability that the firm enters a given undesirable state, such as ceasing production, by imposing a fine which is as low as possible, while achieving the required reduction in probability. The exogenous uncertainty is modeled using a stochastic differential equation, and we show this implies that the firm's behavior can be solved via the Hamilton-Jacobi-Bellman equation, and the regulatory fine can be obtained via the Feynman-Kac formula. We discuss both analytic and numerical solution methods. Our results are illustrated for a security of supply problem for vaccine production where future production costs are uncertain and, using empirical data, for an abandonment problem in a gold mining operation where future commodity prices are uncertain. The method determines the level of fine which establishes a Nash equilibrium in these nonzero-sum games, under uncertainty.

Item Type: MIMS Preprint
Additional Information: Working Paper
Uncontrolled Keywords: Regulation, Uncertainty, Nash Equilibrium, Early Termination, Mining,Vaccine Supply, Optimal Policy
Subjects: MSC 2010, the AMS's Mathematics Subject Classification > 35 Partial differential equations
MSC 2010, the AMS's Mathematics Subject Classification > 60 Probability theory and stochastic processes
MSC 2010, the AMS's Mathematics Subject Classification > 91 Game theory, economics, social and behavioral sciences
Depositing User: Dr Geoff Evatt
Date Deposited: 25 Nov 2011
Last Modified: 08 Nov 2017 18:18
URI: https://eprints.maths.manchester.ac.uk/id/eprint/1708

Available Versions of this Item

Actions (login required)

View Item View Item