Behavioral economics

Economics, like behavioral psychology, is a science of behavior, albeit highly organized human behavior. The value of economic concepts for behavioral psychology rests on (1) their empirical validity when tested in the laboratory with individual subjects and (2) their uniqueness when compared to established behavioral concepts. Several fundamental concepts are introduced and illustrated by reference to experimental data: open and closed economies, elastic and inelastic demand, and substitution versus complementarity. Changes in absolute response rate are analyzed in relation to elasticity and intensity of demand. The economic concepts of substitution and complementarity are related to traditional behavioral studies of choice and to the matching relation. The economic approach has many implications for the future of behavioral research and theory. In general, economic concepts are grounded on a dynamic view of reinforcement. The closed-economy methodology extends the generality of behavioral principles to situations in which response rate and obtained rate of reinforcement are interdependent. Analysis of results in terms of elasticity and intensity of demand promises to provide a more direct method for characterizing the effects of “motivational” variables. Future studies of choice should arrange heterogeneous reinforcers with varying elasticities, use closed economies, and modulate scarcity or income. The economic analysis can be extended to the study of performances that involve subtle discriminations or skilled movements that vary in accuracy or quality as opposed to rate or quantity, and thus permit examination of time/accuracy trade-offs.     

Multilevel analysis of matching behavior

Multilevel modeling has been considered a promising statistical tool in the field of the experimental analysis of behavior and may serve as a convenient statistical analysis for matching behavior because it structures data in groups (or levels) to account simultaneously for the within‐subject and between‐subject variances. Heretofore, researchers have sometimes pooled data erroneously from different subjects in a single analysis by using average ratios, average response and reinforcer rates, aggregation of subjects, etc. Unfortunately, this leads to loss of information and biased estimations, which can severely undermine generalization of the results. Instead, a multilevel approach is advocated to combine several subjects' matching behavior. A reanalysis of previous data on matching behavior is provided to illustrate the method and point out its advantages. It illustrates that multilevel regression leads to better estimations, is more convenient, and offers more behavioral information. We hope this paper will encourage the use of multilevel modeling in the statistical practices of behavior analysts.     

A behavioral economic analysis of concurrently available money and cigarettes.

In economic terms, consumption of a reinforcer is determined by its price and the availability and price of other reinforcers. This study examined the effects of response-requirement (i.e., price) manipulations on the self-administration of two concurrently available reinforcers. Six cigarette smokers participated in 4-hr sessions in which money and puffs on a cigarette were concurrently available according to fixed-ratio schedules of reinforcement. Once stable responding was obtained with both reinforcers available at Fixed Ratio 100, the response requirement for one reinforcer was systematically varied (Fixed Ratio 1,000 and 2,500), while the other reinforcer remained scheduled at Fixed Ratio 100. Increasing the fixed-ratio size for a reinforcer decreased its consumption, with a greater decrease occurring for monetary reinforcement. This finding was quantified in economic terms as own-price elasticity, with elasticity coefficients greater for money than cigarettes. The effects of fixed-ratio size on response output also differed across the two reinforcers. Although greater responding occurred for money at Fixed Ratio 100, increases in fixed-ratio size (for money) decreased responding for money, whereas the same increase in fixed-ratio size (for puffs) increased responding for puffs. Finally, increasing the fixed-ratio size for one reinforcer had little effect on consumption of the other concurrently available reinforcer. This finding was quantified as cross-price elasticity, with elasticity coefficients near 0.0 for most subjects, indicating little or no reinforcer interaction. The results indicate that the reinforcing effects of cigarettes and money in the setting studied here differed, and that the effects produced by changing the price of one reinforcer did not interact with the consumption of the other concurrently available reinforcer.     

Assessment of a response bias for aggression over functionally equivalent appropriate behavior

We evaluated the effects of a dense (fixed-ratio 1) schedule of reinforcement for an 11-year-old boy's mands for toys while aggression produced the same toys on various schedules chosen on the basis of a progressive-ratio probe. Based on the probe session data, we accurately predicted that aggression would be more probable than mands when the schedules were equal or slightly discrepant, but that mands would be more probable when the schedule discrepancy was large.     

Concurrent reinforcement schedules for problem behavior and appropriate behavior: experimental applications of the matching law

This study evaluated how children who exhibited functionally equivalent problem and appropriate behavior allocate responding to experimentally arranged reinforcer rates. Relative reinforcer rates were arranged on concurrent variable-interval schedules and effects on relative response rates were interpreted using the generalized matching equation. Results showed that relative rates of responding approximated relative rates of reinforcement. Finally, interventions for problem behavior were evaluated and differential reinforcement of alternative behavior and extinction procedures were implemented to increase appropriate behavior and decrease problem behavior. Practical considerations for the application of the generalized matching equation specific to severe problem behavior are discussed, including difficulties associated with defining a reinforced response, and obtaining steady state responding in clinical settings.     

Consumer brand choice: individual and group analyses of demand elasticity

Following the behavior-analytic tradition of analyzing individual behavior, the present research investigated demand elasticity of individual consumers purchasing supermarket products, and compared individual and group analyses of elasticity. Panel data from 80 UK consumers purchasing 9 product categories (i.e., baked beans, biscuits, breakfast cereals, butter, cheese, fruit juice, instant coffee, margarine and tea) during a 16-week period were used. Elasticity coefficients were calculated for individual consumers with data from all or only 1 product category (intra-consumer elasticities), and for each product category using all data points from all consumers (overall product elasticity) or 1 average data point per consumer (interconsumer elasticity). In addition to this, split-sample elasticity coefficients were obtained for each individual with data from all product categories purchased during weeks 1 to 8 and 9 to 16. The results suggest that: 1) demand elasticity coefficients calculated for individual consumers purchasing supermarket food products are compatible with predictions from economic theory and behavioral economics; 2) overall product elasticities, typically employed in marketing and econometric research, include effects of interconsumer and intraconsumer elasticities; 3) when comparing demand elasticities of different product categories, group and individual analyses yield similar trends; and 4) individual differences in demand elasticity are relatively consistent across time, but do not seem to be consistent across products. These results demonstrate the theoretical, methodological, and managerial relevance of investigating the behavior of individual consumers.     

The etymology of basic concepts in the experimental analysis of behavior

The origins of many of the basic concepts used in the experimental analysis of behavior can be traced to Pavlov's (1927/1960) discussion of unconditional and conditional reflexes in the dog, but often with substantial changes in meaning (e.g., stimulus, response, and reinforcement). Other terms were added by Skinner (1938/1991) to describe his data on the rate of lever pressing in the rat (e.g., operant conditioning, conditioned reinforcement, negative reinforcement, and response induction and differentiation) and key pecking in the pigeon (shaping). The concept of drive, however, has largely disappeared from the current literature.     

Does preference rank predict substitution for the reinforcer for problem behavior? a behavioral economic analysis

Predictions made under low response requirements inherent in most preference assessments (PAs) do not guarantee the utility of stimuli in treatments. We examined whether PA rank would predict how well stimuli supported compliance for children with escape‐maintained problem behavior by examining the relation between PA rank and demand elasticity across five fixed‐ratio values. Three patterns were observed: All stimuli were selected equally across values, higher ranked stimuli were selected more at higher values, and something other than the highest ranked stimulus was selected more at higher values (i.e., mixed correspondence).     

Interpretation(s) of elasticity in operant demand

This brief report provides an account of varying interpretations of elasticity (η ) in the operant demand framework. General references to “demand elasticity” have existed since the Exponential model of operant demand was proposed by Hursh and Silberberg (2008). This term has been used interchangeably with Essential Value (EV), P_MAX, and the rate of change constant α . This report provides an in-depth account of η and the various ways in which this metric has been used to interpret fitted demand functions. A review of relevant mathematic terms, operations associated with differentiating parameters, and worked solutions for η are provided for linear and nonlinear demand functions. The relations between η and EV, P_MAX, and α are described and explained in terms of their mathematical bases and recommendations are provided regarding their individual interpretation. This report concludes with recommendations for providing additional mathematical detail in published works and emphasizing a consistent use of terms when describing aspects of operant demand.     

Further analysis of the matching law to describe two- and three-point shot allocation by professional basketball players

The matching law was used to analyze whether the proportion of shots taken from two- or three-point range would match the proportional reinforcement rates produced by those shots when the reinforcement rate of three-point shooting was changed. Rule changes in 1994 and 1997 altered the distance of the three-point line in the National Basketball Association, which created a quasiexperimental reversal design, thereby naturally changing three-point reinforcement rates. The present data partially confirmed predictions made by the matching law, in that increases in the relative rate of three-point shots attempted corresponded to increases in the relative rate of three-point shots made.     

Choices between positive and negative reinforcement during treatment for escape-maintained behavior.

Positive reinforcement was more effective than negative reinforcement in promoting compliance and reducing escape‐maintained problem behavior for a child with autism. Escape extinction was then added while the child was given a choice between positive or negative reinforcement for compliance and the reinforcement schedule was thinned. When the reinforcement requirement reached 10 consecutive tasks, the treatment effects became inconsistent and reinforcer selection shifted from a strong preference for positive reinforcement to an unstable selection pattern.     

Computational behavior dynamics: an alternative description of Nevin (1969).

A computational processing behavior-dynamic model was instantiated in the form of a computer program that "behaved" on the task developed by Nevin (1969). In this classic discrete-trials experiment, the relative frequency of choosing a response alternative matched the relative frequency of reinforcement for that alternative, the local structure of responding was opposite that predicted by momentary maximizing (i.e., the probability of a changeover decreased with run length), and absolute and relative response rates varied independently. The behavior-dynamic model developed here qualitatively reproduced these three results (but not in quantitative and specific detail) and also generated some interesting, as-yet-untested predictions about performance in Nevin's task. The model was discussed as an example of a stochastic behavior-dynamic alternative to algebraic behavior theory.     

Reanalysis of the equation for simple action

De Villiers and Herrnstein (1976) have shown that the equation for simple action, derived from the matching law, predicts change in behavioral output for some 40 experiments in which the value of a single source of reinforcement has been varied. Using only the positive-reinforcement studies they cite that used five or more different reinforcement values, we found the high percentage of variance they report accommodated by this equation (94%) is predicated on instances of averaging rates of behavioral output before making a least-squares fit of the equation. In our reanalysis, which minimizes rate averaging, only 78% of the data variance is accommodated. This diminished data-variance accommodation can be improved by adding parameters that permit the equation's scaling constant to change as a function of reinforcement. Although these parameters permit acceptable levels of accommodation of data variance, they correspond to no obvious psychological processes. These findings support the view that the equation for simple action is an inadequate model for behavioral output.     

Zebrafish choice behavior is sensitive to reinforcer rate,immediacy, and magnitude ratios

Behavioral flexibility has, in part, been defined by choice behavior changing as a function of changes in reinforcer payoffs. We examined whether the generalized matching law quantitatively described changes in choice behavior in zebrafish when relative reinforcer rates, delays/immediacy, and magnitudes changed between two alternatives across conditions. Choice was sensitive to each of the three reinforcer properties. Sensitivity estimates to changes in relative reinforcer rates were greater when 2 variable-interval schedules were arranged independently between alternatives (Experiment 1a) than when a single schedule pseudorandomly arranged reinforcers between alternatives (Experiment 1b). Sensitivity estimates for changes in relative reinforcer immediacy (Experiment 2) and magnitude (Experiment 3) were similar but lower than estimates for reinforcer rates. These differences in sensitivity estimates are consistent with studies examining other species, suggesting flexibility in zebrafish choice behavior in the face of changes in payoff as described by the generalized matching law.     

Application of the generalized matching law to chess openings: A gambit analysis

During the opening moves of a chess game, a player (typically White) may offer a number of gambits, which involve sacrificing a chess piece for an opponent for capture to achieve long-term positional advantages. One of the most popular gambits is called the Queen's Gambit and involves White offering a pawn to Black, which will open a lane for White's Queen if accepted by Black. In the present study, the generalized matching law (GML) was applied to chess openings involving the Queen's Gambit using over 71,000 archived chess games. Overall, chess players' opening moves involving the Queen's Gambit exhibited orderly matching as predicted by the GML, and the GML accounted for more variance in players' chess decision making as their relative playing experience increased. This study provides support for the generality of the GML and its application to complex operant behavior outside of laboratory contexts.     

Dynamic response-by-response models of matching behavior in rhesus monkeys

We studied the choice behavior of 2 monkeys in a discrete-trial task with reinforcement contingencies similar to those Herrnstein (1961) used when he described the matching law. In each session, the monkeys experienced blocks of discrete trials at different relative-reinforcer frequencies or magnitudes with unsignalled transitions between the blocks. Steady-state data following adjustment to each transition were well characterized by the generalized matching law; response ratios undermatched reinforcer frequency ratios but matched reinforcer magnitude ratios. We modelled response-by-response behavior with linear models that used past reinforcers as well as past choices to predict the monkeys' choices on each trial. We found that more recently obtained reinforcers more strongly influenced choice behavior. Perhaps surprisingly, we also found that the monkeys' actions were influenced by the pattern of their own past choices. It was necessary to incorporate both past reinforcers and past choices in order to accurately capture steady-state behavior as well as the fluctuations during block transitions and the response-by-response patterns of behavior. Our results suggest that simple reinforcement learning models must account for the effects of past choices to accurately characterize behavior in this task, and that models with these properties provide a conceptual tool for studying how both past reinforcers and past choices are integrated by the neural systems that generate behavior.     

Matching and conditioned reinforcement rate

Attempts to examine the effects of variations in relative conditioned reinforcement rate on choice have been confounded by changes in rates of primary reinforcement or changes in the value of the conditioned reinforcer. To avoid these problems, this experiment used concurrent observing responses to examine sensitivity of choice to relative conditioned reinforcement rate. In the absence of observing responses, unsignaled periods of food delivery on a variable-interval 90-s schedule alternated with extinction on a center key (i.e., a mixed schedule was in effect). Two concurrently available observing responses produced 15-s access to a stimulus differentially associated with the schedule of food delivery (S+). The relative rate of S+ deliveries arranged by independent variable-interval schedules for the two observing responses varied across conditions. The relation between the ratio of observing responses and the ratio of S+ deliveries was well described by the generalized matching law, despite the absence of changes in the rate of food delivery. In addition, the value of the S+ deliveries likely remained constant across conditions because the ratio of S+ to mixed schedule food deliveries remained constant. Assuming that S+ deliveries serve as conditioned reinforcers, these findings are consistent with the functional similarity between primary and conditioned reinforcers suggested by general choice theories based on the concatenated matching law (e.g., contextual choice and hyperbolic value-added models). These findings are inconsistent with delay reduction theory, which has no terms for the effects of rate of conditioned reinforcement in the absence of changes in rate of primary reinforcement.     

Group choice: the ideal free distribution of human social behavior

Group choice refers to the distribution of group members between two choice alternatives over time. The ideal free distribution (IFD), an optimal foraging model from behavioral ecology, predicts that the ratio of foragers at two resource sites should equal the ratio of obtained resources, a prediction that is formally analogous to the matching law of individual choice, except that group choice is a social phenomenon. Two experiments investigated the usefulness of IFD analyses of human group choice and individual-based explanations that might account for the group-level events. Instead of nonhuman animals foraging at two sites for resources, a group of humans chose blue and red cards to receive points that could earn cash prizes. The groups chose blue and red cards in ratios in positive relation to the ratios of points associated with the cards. When group choice ratios and point ratios were plotted on logarithmic coordinates and fitted with regression lines, the slopes (i.e., sensitivity measures) approached 1.0 but tended to fall short of it (i.e., undermatching), with little bias and little unaccounted for variance. These experiments demonstrate that an IFD analysis of group choice is possible and useful, and suggest that group choice may be explained by the individual members' tendency to optimize reinforcement.     

Normalized demand for drugs and other reinforcers.

The concepts of behavioral economics have proven to be useful for understanding the environmental control of overall levels of responding for a variety of commodities, including reinforcement by drug self-administration. These general concepts have implications for the assessment of abuse liability and drug abuse intervention and the formulation of public policy on drug abuse. An essential requirement is the ability to compare the demand for different drugs directly in order to assess relative abuse liability, and to compare demand for the same drug under different environmental and biological interventions to assess their ability to reduce demand. Until now, such comparisons were hampered by the confounding effect of varying drug doses and potencies that prevent quantitative comparisons of demand elasticity--sensitivity of consumption and responding to the constraint of price (effort). In this paper we describe a procedure to normalize demand-curve analysis that permits dose- and potency-independent comparisons of demand across drugs. The procedure is shown to be effective for comparing drug demand within and across the drug classes. The technique permits a quantitative ordering of demand that is consistent with the peak levels of responding maintained by the drugs. The same technique is generalized for the comparison of other types of reinforcers under different biological conditions.