RESOURCE-ALLOCATION BEHAVIOR RESOURCE-ALLOCATION BEHAVIOR by Harvey J. Langholtz Antoinette T. Marty Christopher T. Ball The College of William and Mary Williamsburg, Virginia Eric C. Nolan University of California, Davis Davis, California SPRINGER SCIENCE+BUSINESS MEDIA, LLC Library of Congress Cataloging-in-Publication Data Resource-allocation behavior / by Harvey J. Langholtz [et al.] Langholtz, Harvey J., 1948 p.cm. Includes bibliographical references and indexes. ISBN 978-1-4613-5408-6 ISBN 978-1-4615-1131-1 (eBook) DOI 10.1007/978-1-4615-1131-1 I. Strategic planning, 2. Resource allocation. I. Langholtz. Harvey J.. II. Marty, Antoinette T. III. Ball, Christopher T.. IV. Nolan. Eric C. s HD30 .28 .R464 2002 153.8/3—dc21 2002030087 A C.I.P. Catalogue record for this book is available from the Library of Congress. Copyright © 2003 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2003 Softcover reprint of the hardcover 1st edition 2003 All rights reserved. No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without the written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Permission for books published in Europe: permissions^wkap.nl Permission for books published in the United States of America: permissions ^w kap.com Printed on acid-free paper. Contents Chapter 1 1 An Introduction to Resource-Allocation Behavior Chapter 2 9 The Optimal Model: Linear Programming Chapter 3 19 Resource-Allocation Behavior with Time, Three Dimensions, and Minimums Chapter 4 31 Previous Research: Behavioral Models Follow Normative Models Chapter 5 37 Resource-Allocation Behavior with Various Levels of Information Chapter 6 63 Resource-Allocation Behavior in Harsh and Benign Environments VI Chapter 7 89 Resource-Allocation Behavior when Gains and Losses are Possible Chapter 8 107 Resource-Allocation Behavior when the Objective Function Changes Chapter 9 145 Resource-Allocation Behavior in Commonplace but Complex Tasks Chapter 1( ) 179 Cognitive Strategies for Resource-Allocation Behavior Chapter 11 201 Distributive Justice in Resource-Allocation Chapter 12 235 Conclusions and Future Areas to be Mapped Index 243 Chapter 1 AN INTRODUCTION TO RESOURCE ALLOCATION BEHAVIOR How do people make decisions about the allocation of resources? How well or how poorly can people make such decisions? How efficiently can people allocate resources? What are some of the different situations under which people make resource-allocation decisions, and will performance vary between situations? What are some of the cognitive strategies people use to make resource-allocation decisions? Is there a normative standard to which people's performance can be compared in resource-allocation situations? In the 12 chapters of this book we will attempt to address these questions and see how people make resource-allocation decisions. Of course it must be recognized at the outset that such an initial investigation of the topic of resource-allocation behavior can only explore some of preliminary topics, and the results of this preliminary investigation can only be a short list of answers with a longer list of topics for further research. But it is our goal here to build an initial foundation, answer some preliminary questions, propose some possible models and approaches for the investigation of resource-allocation behavior, and suggest what broad areas need to be investigated further. What is Resource-Allocation Behavior, Really? Resource-allocation decisions are ubiquitous, and resource-allocation behavior is continuous. How will we divide our time between the various activities of daily life? How much of today will be spent on working, e-mails, phone calls, meetings, reading, writing, relaxing, exercising, eating, and 2 Chapter 1 sleeping? How much time will we allocate towards our own goals and how much will we provide in service to causes we value? How much of our financial budget will we allocate to food, clothing, shelter, education, recreation, travel, savings, health, charity, and a long list of other priorities? And even within any one of these broad categories, how much of the budgeted money will be allocated to each individual item within that category? And will we consume our resources evenly across time, will we hoard them for future use, or will we squander them early in a time period? Resource-allocation decisions are any decisions in which people make judgments about how they will allocate resources. Resource-allocation behavior is the outward, observable behavior in which people act upon their resource-allocation decisions. While the most common resource-allocation decisions are typically made about time and money, these two resources are in no way the only resources about which we make resource-allocation decisions. Often, the actual application in real-world situations will take forms other than time and money (i.e., how to mix chemicals in an industrial setting, who to hire, which meetings to attend, and where to eat). Chemicals, employees, meetings, and food will be the specific resources to be allocated, but in many cases these resource-allocation decisions are ultimately decisions about the allocation of time or money, or both. There is a normative model for calculating the optimal solution to resource-allocation problems. This optimal solution can be calculated when the costs of the resources are known, when it is understood how the resources may be combined to reach an objective, and when it is possible to place a value on reaching the objective. The method for calculating the optimal solution to such resource-allocation problems is known in the literature of Operations Research (or Management Science) as Linear Programming (LP), (Dantzig, 1963). LP was developed in the late 1940's as a method for determining optimal solutions to large-scale logistical or scheduling problems, and since Dantzig's seminal book on the topic, there have been hundreds of other books and perhaps thousands of joumal articles on the topic. Chapter 2 will provide an introduction to LP, but for a more thorough discussion of the topic, the reader is referred to any of the texts used to teach Operations Research or Management Science, and typically available in any college bookstore for use management or math classes. While it may initially seem unusual to be conducting research on behavior as compared to a model of Operations Research, it is not LP that is being AN INTRODUCTION TO RESOURCE-ALLOCATION BEHAVIOR 3 studied here, but rather people's decision-making behavior as compared to LP. This is no different from the predominance of current publications in the field of decision theory that examine behavior in what are fundamentally Bayesian environments of choice. In such Bayesian environments, the decision maker has a choice of two or more alternatives. Often the decision is made with a knowledge of the possible outcomes and associated probabilities. Decision making in a Bayesian environment will typically be discussed in the same Operations Research texts that teach LP, but Bayesian topics will be discussed in the preliminary chapters and serve as an introduction to the more complicated areas of Operations Research that include LP, Stochastic Processes, Markov Chains, and other models. This Book Is about Behavior, Not Math In the chapters that follow, we examine people's resource-allocation behavior in several different resource-allocation settings. In order to do this properly and thoroughly, we have described the resource-allocation problems using precise LP mathematical format. This may sometimes create the impression that this is a book about LP or math. It is not. This is a book about behavior. But in order to study rigorously, and in order to deconstruct how people make resource-allocation decisions in a methodological way, we must be clear on the normative mathematical model that provides both the optimal solution, but perhaps more importantly, a way for us to view and understand the resource-allocation problem. In order to introduce the concept and the details of LP, this chapter will discuss LP in general conceptual terms. Chapter 2 will introduce LP as a mathematical model. And in Chapter 3 we will examine various types of resource-allocation problems, both static and dynamic. For those already familiar with LP, this may seem like a long, slow, and indirect approach to the topic. And of course if the topic of this book were LP as a mathematical model, it would be a long, slow and indirect approach. But since it is anticipated that most readers of this book will be interested in resource-allocation behavior primarily, and LP only secondarily as an optimal model to be used as a standard against which to compare behavior, this longer approach of examining at resource-allocation behavior in various settings will be taken. This book is about behavior, not math. 4 Chapter 1 Different Types of Resource-Allocation Situations There are many different types of resource-allocation situations or resource-allocation problems. At the most basic level, they can be separated into two categories: maximization problems and minimization problems. In maximization problems the resources are fixed. In minimization problems the goal is fixed. In a maximization problem the decision maker is attempting to reach the maximum possible level of a goal while not consuming more than a fixed amount of resources. For example, the dispatcher for a police department is trying to determine how to obtain the maximum number of hours of police officers on patrol within the fixed budget for salary. Or the planner for a large commercial airline is attempting to schedule a fleet of a fixed size in order to offer the most possible passenger-miles. For some maximization problems it may be more difficult to quantify benefit. For example, the physician in the emergency room is allocating his time between incoming patients. The model for making this resource-allocation decision may be a complex one that involves diagnosis, prioritization, treatment, etc. But even applying LP to determine the optimal solution to such a decision might require numerous assumptions and stipulations. Once those assumptions and stipulations have been accepted, the optimal solution can be calculated. In minimization problems, the decision maker is attempting to reach a fixed goal while consuming a minimum amount of resources. The contractor constructing a building will seek the most economical way to complete the task within the design specifications, but the goal is not to build a smaller building within a fixed budget, but rather to meet a specific task while minimizing costs. Once the airline company planner has determined the best routes to maximize passenger-miles it will be up to the pilot to operate the jet liner in such a way as to minimize fuel consumption. In this book we wiIl examine only maximization problems, not because minimization problems are not important, but for a standard approach to the research. Certainly, for every aspect of resource-allocation behavior that we might study in a maximization situation, we could also examine the same aspect in a minimization situation. For a discussion of resource-allocation in AN INTRODUCTION TO RESOURCE-ALLOCATION BEHAVIOR 5 minimization situations the reader is referred to Gonzalez, Langholtz, and Sopchak (in press). Chapter 3 will begin with a discussion of simple one-time resource-allocation problems. In such problems the decision maker is given a fixed amount of resources and determines the method of allocation that will provide the maximum result. This resource-allocation decision is made once and is not ongoing. An example of such a situation would be an owner of a small fleet of oceangoing commercial fishing vessels, each with different characteristics in terms of size, nets, etc. The fleet owner must decide where to allocate individual boats within an expanse of ocean to fish during a 24-hour legal opening. He must have his vessels prepositioned at specific locations at the start of the legal opening. Once the fishing period begins, they put out their nets, and leave them out for 24 hours, and retrieve them at the end of the 24-hour period. His goal is to harvest the maximum amount of fish but once the decision is made of where each boat will fish, the decision is carried out without revision. In many cases, resource-allocation decisions are made over time, with the decision maker having the benefit of knowing what happened during earlier resource-allocation decisions. These are multi-cycle problems, where each allocation represents one cycle among a series. An example of this would include decisions about how to allocate money from the food budget and where to shop for food or at which restaurant to eat. The decision maker knows how much money remains in the week's food budget; he knows about the local restaurants; and he knows how much food he has at home in his refrigerator. He makes a decision about the next meal based at least on these factors, especially considering how much money remains in the week's food budget and how many days remain until the next payday. Once that allocation cycle is complete, he can wait until the next meal, consider again how much money is left, what his choices are, and where to eat. These allocation cycles are repeated until receipt of the next paycheck. In those situations where the decision maker has the option of carrying over unused resources from one time-frame to the next (or in some cases, borrowing against the next time-frame) the resource-allocation problem is extended. Because carry-over (or borrowing against the future) is possible, this case is actually one ongoing resource-allocation problem, not a series of discrete problems. However, this should be differentiated from those cases where the decision maker will solve a series of identical resource-allocation problems where carry-over (or borrowing) is not permitted. Such problems would include