Multibidding game under uncertainty

This paper considers situations in which a group of agents has to decide whether to carry out a given public project or its alternative when agents hold private information. I use the budget-balanced multibidding mechanism (Pérez-Castrillo and Wettstein in Am Econ Rev 5:1577–1587, 2002) according to which the game to be played by participants has only one stage and simple rules that can be applied in a wide range of situations. It is shown that the symmetric equilibria of the multibidding game deliver ex-post efficient outcomes if the number of agents is two, for any underlying symmetric distribution characterizing uncertainty, or is very large.     

Consistent choice under uncertainty

Suppose ex post preferences are defined upon prizes and ex ante preferences are defined upon lotteries. Then the consistent choice of decision rules reigns whenever ex post optimality is equivalent to ex ante optimality. This essay provides a necessary and sufficient condition for consistent choice in terms of revealed preferences. Indeed, ex ante revealed preferences must be induced from ex post revealed preferences in a manner which requires them to satisfy the independence axiom from expected utility theory.     

Forecast combinations under structural break uncertainty

This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single post-break estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.     

Supplier incentives and resource constraints under uncertainty

Procurement in environments of cost uncertainty and asymmetric information require special arrangements such as the linear incentive contract. Usually the buyer is motivated to make investments that can relieve temporary supplier resource constraints during the procurement. Special problems arise, however, due to interactions between investments in suppliers and the risk-incentive trade-off achieved by the incentive contract. A cost signaling model is proposed to overcome these problems, where a supplier offers an equity share in the profit from the incentive contract to the buyer in return for a priori investment. The equity share signals the supplier's private cost information, and forms the basis for the buyer's investment decision. Under equilibrium the buyer can expect to recover the entire amount provided to the supplier through his or her share of the profit.     

Residential mobility and job changes under uncertainty

This paper empirically investigates households' residential mobility and job change decisions under uncertainty. We allow households' degree of risk aversion to be a confounding factor in the joint decision of residential mobility and job changes. Using panel data to estimate a random effects multinomial probit model of households' joint decision of residential and job mobility, our empirical results show that risk aversion discourages a household from making any changes. Moreover, when compared to single changes in either job or residential locations, risk aversion is more discouraging for joint changes to more central residential locations and less discouraging for joint changes to more distant residential locations. These effects are statistically significant, albeit small in magnitude. Our empirical results demonstrate the uncertainty does play a role in households' job and residential mobility decisions.     

Production under uncertainty: a simulation study

In this paper we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without being explicit about the nature of producer risk preferences. In our model producers’ risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technology we show that rational producers who encounter the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate data based on our state-general state-contingent specification of technology. Biased estimates of the technology parameters are obtained when we apply conventional ordinary least squares estimator on the simulated data.     

Robustness as a corporate objective under uncertainty

This paper examines the nature of the objective function of the firm when operating under conditions of uncertainty. Robustness is presented as a purposeful maximand for decision making both under conditions of certainty and uncertainty - a robust decision being one in which the decision maker retains the maximum flexibility with regard to future decisions after an initial decision has been made. Its incorporation within a managerial objective function provides a measurable scale for making choices between alternative courses of action, including under conditions of internal organizational conflict and environmental reaction by other decision makers.     

Location theory of the firm under price uncertainty

This paper presents a theory of location under price uncertainty employing a general utility and production function. The analysis is also conducted by incorporating a homogeneous production function. Risk preferences of the firm are treated as an integral part of the model.     

An optimal insurance design problem under Knightian uncertainty

This paper solves an optimal insurance design problem in which both the insurer and the insured are subject to Knightian uncertainty about the loss distribution. The Knightian uncertainty is modeled in a multi-prior g-expectation framework. We obtain an endogenous characterization of the optimal indemnity that extends classical theorems of Arrow (Essays in the Theory of Risk Bearing. Markham, Chicago 1971) and Raviv (Am Econ Rev 69(1):84–96, 1979) in the classical situation. In the presence of Knightian uncertainty, it is shown that the optimal insurance contract is not only contingent on the realized loss but also on another source of uncertainty coming from the ambiguity.     

Decision-making under uncertainty for capital budgeting and hiring

Most business decisions are not merely risky but are subject to uncertainty. Different individuals estimate different measures of merit. It is shown that the traditional procedure of ranking the alternatives by their estimated measure of merit and then choosing the highest ranked alternative frequently gives the wrong answer even where the estimates are unbiased. The argument will be made using a simple decision tree. The effect will be shown to be especially serious where there is competition, direct or indirect, between the alternatives.     

Lender learning and entry under general demand uncertainty

In this paper, we examine the effect of potential entry on learning by a lender when the demand shock has a general distribution. We show that under this type of noise, entry does not lead to any changes in the equilibrium expected signals and therefore, there is no effect on learning by the lender, unlike the case when noise is uniformly distributed. The result holds even when contracts are not observable.     

Solid waste planning under uncertainty using evolutionary simulation-optimization

A key aspect of effective public planning design is to minimize the impact of negative outcomes that can arise from the violation of pre-established system constraint criteria. These planning situations can be especially complicated when several components within the studied system are either unknown or contain considerable stochastic uncertainty. In this paper, the concept of outcome minimization through the use of penalty functions is combined with grey programming (GP) into an evolutionary simulation-optimization (ESO) procedure in order to solve solid waste management problems containing significant sources of uncertainty. By employing outcome minimization concurrently with GP and ESO, it can be shown that plans that meet, or come close to meeting, required system criteria can be efficiently created. The efficacy of the procedure is demonstrated through its application to a solid waste planning case from the Municipality of Hamilton-Wentworth in the Province of Ontario, Canada. Since ESO techniques can be adapted to a wide variety of problem types in which some or all of the system components are stochastic, the practicality of this approach can be adapted to many operational and strategic planning situations containing significant sources of uncertainty.     

Optimal harvesting under resource stock and price uncertainty

We analyze partial and complete depletion harvesting policy under resource stock and price uncertainty and risk neutrality. We state a set of weak conditions under which the optimal policy can be characterized by a single threshold and show that the value can be expressed in a separable form where price volatility affects the value through the risk adjusted growth rate. Both higher price and stock volatility decrease the value when the correlation between the driving Brownian motions is negative. With no correlation the optimal policy is independent of price volatility while higher stock volatility increases the harvesting threshold.     

Sequential decisions under uncertainty and the maximum theorem

For sequential decision problems in which the decision-maker observes a process of state variables and chooses an adapted process of action variables, the paper defines a topology on the space of measures of processes of state variables which ensures the applicability of Berge's maximum theorem to the decision-maker's optimal behavior. The topology controls for the information available to the decision-maker at each decision date. The paper also discusses the implications of the analysis for the dynamic-programming approach to sequential decision-making under uncertainty, and for equilibrium existence proof strategies in sequential-market models and games.     

Industry analysis and investment decision-making under conditions of uncertainty

Evaluating risk is a key element in successful investment decision-making. A major risk in forecasting company performance is associated with projecting its cash flow streams by product-line which in turn is strongly related to the expected industry outlook and likely variability about this outlook. This paper sets out a methodology for evaluating this aspect of the investment decision by developing measures of expected real cash flow growth (reward) and expected annual variability of this growth (risk). These measures are constructed for 77 industry classifications at the two and three digit SIC (Standard Industrial Classification) level of detail by solving a dynamic input–output model under various economic secenarios. Aside from producing results which are relevant for producing company valuation bands, the analysis strongly suggests that perceptions of which industries are ‘winners’ and which are ‘losers’ are significantly altered when both ‘reward’ and ‘risk’ are used as criteria as opposed to either one alone. Finally, the methodology also produces results which measures the sources of cash flow growth in terms of relative price performance, productivity, and demand for output. Since there measures can be used as indicators of the quality of industry real cash flow growth, industry performance can be further arrayed within the broad categories of winning and losing industries. Several examples of how this is done are offered.     

Property value impacts of foreclosed housing acquisitions under uncertainty

Community development corporations seek to stabilize neighborhoods affected by the recent foreclosure crisis through acquisition and redevelopment of distressed properties. One rationale for this work is the alleviation or avoidance of negative foreclosure impacts. We estimate the lost value to proximate properties associated with a single foreclosure through a Markov chain representing probabilistic transitions between foreclosure stages. We apply our model to a case study of foreclosure properties in Chelsea, MA. A rank ordering by estimated property value impacts indicates significant potential gains in social value as compared to current community development practice. We extend our basic model to address the effects of clusters of foreclosed units upon the value of proximate properties. This study provides additional support for the use of decision modeling in foreclosed housing acquisition and redevelopment.     

Numerical solution of dynamic equilibrium models under Poisson uncertainty

We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retarded type. We apply the Waveform Relaxation algorithm, i.e., we provide a guess of the policy function and solve the resulting system of (deterministic) ordinary differential equations by standard techniques. For parametric restrictions, analytical solutions to the stochastic growth model and a novel solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households.     

Optimal capital accumulation under price uncertainty and costly reversibility

We consider the optimal capital accumulation policy of a competitive firm operating in the presence of decreasing returns to scale, price uncertainty, and costly reversibility of investment. We characterize the optimal accumulation policy and derive the value of the firm by focusing on the marginal investment decision and solving the associated optimal timing problem characterizing the option value of the associated opportunity to either disinvest or acquire a marginal unit of capacity. We also characterize the required exercise premia associated with the optimal policies and demonstrate that hysteresis prevails within this class of accumulation problems as well.