Technology choice under several uncertainty sources

We analyze a model of irreversible investment with two sources of uncertainty. A risk-neutral decision maker has the choice between two mutually exclusive projects under input price and output price uncertainty. We propose a complete study of the shape of the rational investment region and we prove that it is never optimal to invest when the alternative investments generate the same payoff independently of its size. A key feature of this bidimensional degree of uncertainty is thus that the payoff generated by each project is not a sufficient statistic to make a rational investment. In this context, our analysis provides a new motive for waiting to invest: the benefits associated with the dominance of one project over the other. As an illustration, we apply our methodology to power generation under uncertainty.     

Predicted violations of the invariance principle in choice under uncertainty

A basic and distinctive prediction of regret theory is that certain variations in the juxtaposition of the consequences of alternative actions will tend to affect preferences between them in a particular way. This paper presents a body of experimental evidence which tests that prediction.The evidence in this paper comes from experimental work financed by the Economic and Social Research Council (U.K.), Awards B 00 23 2127 and B 00 23 2163. The author would also like to thank the British Academy and the University of York for assistance with the costs of travel and attendance at the Symposium.     

在未来收益不确定下的期权定价模型

§ 1　 IntroductionAt present,some high-technology(high-tech) firms such as information,software,pharmaceutical et al,are rapidly growing at home and abroad.The firm' s stock price hasbeen bid upward irrationally by individual day traders.Some managers see the currentfren-zy as a spectacularexample ofmarketbubble.The marketbubble can' tbe explained by theaverage rate of growth,butithas reacted on the developmentprospect of high-tech firms.Existing net cash flow method cannot properly capture …     

An average lexicographic value for cooperative games

For games with a non-empty core the Alexia value is introduced, a value which averages the lexicographic maxima of the core. It is seen that the Alexia value coincides with the Shapley value for convex games, and with the nucleolus for strongly compromise admissible games and big boss games. For simple flow games, clan games and compromise stable games an explicit expression and interpretation of the Alexia value is derived. Furthermore it is shown that the reverse Alexia value, defined by averaging the lexicographic minima of the core, coincides with the Alexia value for convex games and compromise stable games.     

With uncertainty by choice of zero dynamics

We propose an approach to output stabilization of multiply connected control systems with uncertainty based on structural decomposition of the original system and asymptotic invariance methods. The proposed approach solves the stabilization problem for minimum-phase systems. Bounds are obtained on the rate of convergence of the stabilization algorithms. Conditions are derived expanding the class of vector controlled systems with uncertainty that are stabilizable by asymptotic invariance methods.     

Foundations of lexicographic utility

This paper presents an alternative mathematical characterization of lexicographic utility to the one given by Chipman (1960). A natural constructivistic procedure on imposing a lexicographic ordering on the product space of natural numbers is pursued. The consequences on the topological structure of such a space are examined.     

Automorphisms of lexicographic products

The automorphism group Γ(P) of a partially ordered set P consists of all permutations on P that preserve order (and have order preserving inverses). In this paper we raise, and partially answer, the question: How is the automorphism group of the lexicographic product (P × Q) of two orders (P and Q) related to the automorphism groups of the factors, P and Q?We show that the wreath product Γ(Q) wr ΓP) is always contained in Γ(P × Q). We find necessary and sufficient conditions for Γ(P × Q) to equal Γ(Q) wr Γ(P), and when P and Q are finite, we find a complete characterization of Γ(P × Q) in terms of Γ(P), Γ(Q) and properties of P and Q.     

Dynamic journeying under uncertainty

We introduce a journey planning problem in multi-modal transportation networks under uncertainty. The goal is to find a journey, possibly involving transfers between different transport services, from a given origin to a given destination within a specified time horizon. Due to uncertainty in travel times, the arrival times of transport services at public transport stops are modeled as random variables. If a transfer between two services is rendered unsuccessful, the commuter has to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process in which states are defined as paths in the transport network. The main contribution is a backward induction method that generates an optimal policy for traversing the public transport network in terms of maximizing the probability of reaching the destination in time. By assuming history independence and independence of successful transfers between services we obtain approximate methods for the same problem. Analysis and numerical experiments suggest that while solving the path dependent model requires the enumeration of all paths from the origin to the destination, the proposed approximations may be useful for practical purposes due to their computational simplicity. In addition to on-time arrival probability, we show how travel and overdue costs can be taken into account, making the model applicable to freight transportation problems.     

An application of Lagrangian relaxation to a capacity planning problem under uncertainty

A supply chain network-planning problem is presented as a two-stage resource allocation model with 0-1 discrete variables. In contrast to the deterministic mathematical programming approach, we use scenarios, to represent the uncertainties in demand. This formulation leads to a very large scale mixed integer-programming problem which is intractable. We apply Lagrangian relaxation and its corresponding decomposition of the initial problem in a novel way, whereby the Lagrangian relaxation is reinterpreted as a column generator and the integer feasible solutions are used to approximate the given problem. This approach addresses two closely related problems of scenario analysis and two-stage stochastic programs. Computational solutions for large data instances of these problems are carried out successfully and their solutions analysed and reported. The model and the solution system have been applied to study supply chain capacity investment and planning.     

Forestry management under uncertainty

The forest harvest and road construction planning problem consists fundamentally of managing land designated for timber production and divided into harvest cells. For each time period the planner must decide which cells to cut and what access roads to build in order to maximize expected net profit. We have previously developed deterministic mixed integer linear programming models for this problem. The main contribution of the present work is the introduction of a multistage Stochastic Integer Programming model. This enables the planner to make more robust decisions based on a range of timber price scenarios over time, maximizing the expected value instead of merely analyzing a single average scenario. We use a specialization of the Branch-and-Fix Coordination algorithmic approach. Different price and associated probability scenarios are considered, allowing us to compare expected profits when uncertainties are taken into account and when only average prices are used. The stochastic approach as formulated in this work generates solutions that were always feasible and better than the average solution, while the latter in many scenarios proved to be infeasible.     

Cooperation under interval uncertainty

In this paper, the classical theory of two-person cooperative games is extended to two-person cooperative games with interval uncertainty. The core, balancedness, superadditivity and related topics are studied. Solutions called ψ ^α-values are introduced and characterizations are given.     

A proof of a conjecture of Sabidussi on graphs idempotent under the lexicographic product

In 1960, Sabidussi conjectured that if a graph G is isomorphic to the lexicographic product G[G], then the wreath product of by itself is a proper subgroup of . A positive answer is provided by constructing an automorphism Ψ of G[G] which satisfies: for every vertex x of G, there is an infinite subset I(x) of V(G) such that Ψ({x}×V(G))=I(x)×V(G).     

MRP-controlled batch-manufacturing environment under uncertainty

The overall aim of this research is to model the effects of uncertainty on delivery performance in an MRP-controlled batch-manufacturing environment with multi-product and multi-level dependent demand. To this end, MRP planning and batch-manufacturing system control architectures were modelled using simulation to implement the MRP release logic. Simulation and experimental design were carried out based on a real case enterprise. ANOVA showed that four uncertainty factors—namely late delivery from suppliers, machine breakdowns, process batch size increments and customer design changes—have significant effects on delivery performance. This ANOVA further showed that uncertainties create knock-on and compound effects; the latter are difficult to predict in practice. Significant two-way and three-way interactions among some uncertainty factors were also found, making it more difficult to characterise the precise factor effects. It was found that the more uncertain the environment is, the later the deliveries are. It can be concluded that MRP-controlled batch-manufacturing enterprises should diagnose uncertainties that are significantly affecting delivery performance, and tackle these uncertainties most urgently to prevent diffusion of knock-on and compound effects and improve delivery performance. This conclusion was validated through the case enterprise, for which significant delivery improvement has been achieved.     

Fatou closedness under model uncertainty

We provide a characterization in terms of Fatou closedness for weakly closed monotone convex sets in the space of \({\mathcal P}\)-quasisure bounded random variables, where \({\mathcal P}\) is a (possibly non-dominated) class of probability measures. Applications of our results lie within robust versions the Fundamental Theorem of Asset Pricing or dual representation of convex risk measures.     

Multicriteria decision making under uncertainty

Formalization for problems of multicriteria decision making under uncertainty is constructed in terms of guaranteed and weak estimates. A relevant definition of the vector maximinimax value is given. Parameterization and approximation of maximum, minimax, and maximinimax values based on the inverse logical convolution are suggested. An application for multicommodity networks is considered. Received: December 13, 2000 / Accepted: August 21, 2001?Published online May 8, 2002