Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

On the Preference to Avoid Ex Post Inventory Errors

The value of demand information underlies many supply chain strategies that aim at better matching supply and demand. This study reports on the results of a laboratory experiment designed to estimate the behavioral value of demand information. Relative to the commonly assumed benchmark of a rational risk‐neutral decision maker, we find that decision makers are consistently willing to pay too much for the option to eliminate the risk of supply not matching demand. Contrary to intuition, we show that risk aversion does not explain this result. We posit that demand information provides behavioral value because it mitigates regret from ex post inventory errors.     

Decision Making and the Price Setting Newsvendor: Experimental Evidence

We present an experimental study of the price‐setting newsvendor problem, which extends the traditional framework by allowing the decision maker to determine both the selling price and the order quantity of a given item. We compare behavior under this model with two benchmark conditions where subjects have a single decision to make (price or quantity). We observe that subjects deviate from the theoretical benchmarks when they are tasked with a single decision. They also exhibit anchoring behavior, where their anchor is the expected demand when quantity is the decision variable and is the initial inventory level when price is the decision variable. When decision makers set quantity and price concurrently, we observe no significant difference between the normative (i.e., expected profit‐maximizing) prices and the decision makers’ price choices. Quantity decisions move further from the normative benchmarks (compared to when subjects have a single decision to make) when the ratio of cost to price is less than half. When this ratio is reversed, there is no significant difference between order levels in single‐ and multi‐task settings. In the multidecision framework, we also observe a tendency to match orders and expected demand levels, which subjects can control using prices.     

Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.     

Corporate Strategy Formulation and Taxation: Evidence from UK Firms*

This paper considers the relationship between corporate strategy formulation and taxation. Drawing on prior literature we briefly review the important influence that international taxation has on an array of corporate strategy decisions. We then consider issues in strategy formulation and taxation planning in order to develop an understanding of when and how taxation factors impinge on strategic decision‐making. We draw out the apparent paradox between the nature of strategic decision‐making and financial decision‐making and look for areas of reconciliation. In order to shed light on some of these issues we present findings from the qualitative analysis of a set of personal interviews undertaken with senior tax practitioners in seven UK‐based multinational enterprises and then consider quantitative responses from the tax practitioners working in 145 UK firms.     

An Entropy‐Based Methodology for Valuation of Demand Uncertainty Reduction

We propose a distribution‐free entropy‐based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology's use in three different settings: (i) a newsvendor valuing knowledge of expected demand, (ii) a short life cycle product supply manager considering the adoption of a quick response strategy, and (iii) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.     

Preferences for One‐Shot Resolution of Uncertainty and Allais‐Type Behavior

Experimental evidence suggests that individuals are more risk averse when they perceive risk that is gradually resolved over time. We address these findings by studying a decision maker who has recursive, nonexpected utility preferences over compound lotteries. The decision maker has preferences for one‐shot resolution of uncertainty if he always prefers any compound lottery to be resolved in a single stage. We establish an equivalence between dynamic preferences for one‐shot resolution of uncertainty and static preferences that are identified with commonly observed behavior in Allais‐type experiments. The implications of this equivalence on preferences over information systems are examined. We define the gradual resolution premium and demonstrate its magnifying effect when combined with the usual risk premium.     

Supply Chain Contract Design: Impact of Bounded Rationality and Individual Heterogeneity

In this article, we model various forms of non‐optimizing behavior in a newsvendor setting, including biases such as recency, reinforcement, demand chasing, and anchoring, as well as unsystematic decision errors. We assume that a newsvendor may evaluate decisions by examining both past outcomes and future expected payoffs. Our model is motivated by laboratory observations under several types of supply chain contracts. Ordering decisions are found to follow multi‐modal distributions that are dependent on contract structures and incentives. We differ from previous research by using statistics to determine which behavioral factors are applicable to each decision maker. A great deal of heterogeneity was discovered, indicating the importance of calibrating a contract to the individual. Our analysis also shows that the profit performance and the effectiveness of co‐ordinating contracts can be affected by non‐optimizing behaviors significantly. We conclude that, in addition to the aggregate order quantities, the decision distributions should be considered in designing contracts.     

LEARNING TO REOPTIMIZE CONSUMPTION AT NEW INCOME LEVELS: A RATIONALE FOR PROSPECT THEORY

This paper provides a theoretical rationale for three experimental results of Prospect Theory: risk preferences are over gains and losses, loss aversion, and diminishing sensitivity. We consider a (boundedly rational) decision maker who does not find her new optimal consumption bundle with certainty when she is faced with a new income level. This alters her indirect utility function and makes her more risk averse at her current reference income level and less risk averse for a range of incomes below her reference income level. (JEL: D11)     

Quantitative Comparison of Approximate Solution Sets for Bi‐criteria Optimization Problems*

We present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near‐pareto‐optimal solutions to bi‐criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density function provided by a decision maker and a discrete set of solutions for a particular problem, assigns a numerical value to that solution set. This value can be used to compare the quality of different sets of solutions, and therefore provides a robust, quantitative approach for comparing different heuristic, a posteriori solution procedures for difficult multiple objective optimization problems. We provide specific examples of decision maker preference functions and illustrate the calculation of the resulting IPF for specific solution sets and a simple family of combined objectives.     

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Although a large body of research has examined the effects of unions on the wage distribution, surprisingly little attention has been devoted to the effects of unions on the distribution of income. This paper examines the long‐run relationship between unionization and income inequality for a sample of 20 countries. Using heterogeneous panel co‐integration and causality techniques, we find that (i) unions have, on average, a negative long‐run effect on income inequality, (ii) there is considerable heterogeneity in the effects of unionization on income inequality across countries (in 40 per cent of the cases the effect is positive) and (iii) long‐run causality runs in both directions, suggesting that, on average, an increase in unionization reduces income inequality and that, in turn, higher inequality leads to lower unionization rates.     

Cross‐Training with Imperfect Training Schemes

Cross‐training workers is one of the most efficient ways of achieving flexibility in manufacturing and service systems for increasing responsiveness to demand variability. However, it is generally the case that cross‐trained employees are not as productive on a specific task as employees who were originally trained for that task. Also, the productivity of the cross‐trained workers depends on when they are cross‐trained. In this work, we consider a two‐stage model to analyze the effects of variations in productivity levels on cross‐training policies. We define a new metric called achievable capacity and show that it plays a key role in determining the structure of the problem. If cross‐training can be done in a consistent manner, the achievable capacity is not affected when the training is done, which implies that the cross‐training decisions are independent of the opportunity cost of lost demand and are based on a trade‐off between cross‐training costs at different times. When the productivities of workers trained at different times differ, there is a three‐way trade‐off between cross‐training costs at different times and the opportunity cost of lost demand due to lost achievable capacity. We analyze the effects of variability and show that if the productivity levels of workers trained at different times are consistent, the decision maker is inclined to defer the cross‐training decisions as the variability of demand or productivity levels increases. However, when the productivities of workers trained at different times differ, an increase in the variability may make investing more in cross‐training earlier more preferable.     

The Dynamics of Inequality

The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the long‐run income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: “scale dependence” that may arise from changes in skill prices, and “type dependence,” that is, the presence of some “high‐growth types.” These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of “superstar” entrepreneurs or managers.     

Foundations of Intrinsic Habit Formation

We provide theoretical foundations for several common (nested) representations of intrinsic linear habit formation. Our axiomatization introduces an intertemporal theory of weaning a decision‐maker from her habits using the device of compensation. We clarify differences across specifications of the model, provide measures of habit‐forming tendencies, and suggest methods for axiomatizing time‐nonseparable preferences.     

The Role of Automatic Stabilizers in the U.S. Business Cycle

Most countries have automatic rules in their tax‐and‐transfer systems that are partly intended to stabilize economic fluctuations. This paper measures their effect on the dynamics of the business cycle. We put forward a model that merges the standard incomplete‐markets model of consumption and inequality with the new Keynesian model of nominal rigidities and business cycles, and that includes most of the main potential stabilizers in the U.S. data and the theoretical channels by which they may work. We find that the conventional argument that stabilizing disposable income will stabilize aggregate demand plays a negligible role in the dynamics of the business cycle, whereas tax‐and‐transfer programs that affect inequality and social insurance can have a larger effect on aggregate volatility. However, as currently designed, the set of stabilizers in place in the United States has had little effect on the volatility of aggregate output fluctuations or on their welfare costs despite stabilizing aggregate consumption. The stabilizers have a more important role when monetary policy is constrained by the zero lower bound, and they affect welfare significantly through the provision of social insurance.     

Expected Uncertain Utility Theory

We introduce and analyze expected uncertain utility (EUU) theory. A prior and an interval utility characterize an EUU decision maker. The decision maker transforms each uncertain prospect into an interval‐valued prospect that assigns an interval of prizes to each state. She then ranks prospects according to their expected interval utilities. We define uncertainty aversion for EUU, use the EUU model to address the Ellsberg Paradox and other ambiguity evidence, and relate EUU theory to existing models.     

风险规避型供应链的收益共享机制研究

探讨了由风险规避型零售商与风险规避型供应商组成的两级供应链协作激励问题.首先研究了Stackelberg主从对策模型下风险规避型零售商与风险规避型供应商的决策行为,并分析了双方的风险规避系数对双方决策行为的影响.然后,研究了垂直一体化模型下风险规避型供应链的决策行为,同样分析了双方风险规避系数对供应链决策行为的影响程度.最后,提出采用收益共享契约机制来协调该供应链.并通过数值实验进一步分析供应商与零售商的风险规避系数对双方的决策行为及收益共享系数的影响程度.     

Altruism in Networks

We provide the first analysis of altruism in networks. Agents are embedded in a fixed network and care about the well‐being of their network neighbors. Depending on incomes, they may provide financial support to their poorer friends. We study the Nash equilibria of the resulting game of transfers. We show that equilibria maximize a concave potential function. We establish existence, uniqueness of equilibrium consumption, and generic uniqueness of equilibrium transfers. We characterize the geometry of the network of transfers and highlight the key role played by transfer intermediaries. We then study comparative statics. A positive income shock to an individual benefits all. For small changes in incomes, agents in a component of the network of transfers act as if they were organized in an income‐pooling community. A decrease in income inequality or expansion of the altruism network may increase consumption inequality.