Theoretical and experimental examinations of target-based decision making

Target based decision making occurs when a decision maker wishes to maximize the probability of attaining some output or performance level. The target may be set externally, for example, by an employer as part of an incentive system. Or, the target may simply be based on a decision maker’s individual goals and understanding of implicit expectations. Questions arise concerning the effects of these decisions on the performance of an organization. Further, if these decisions affect organizational value, how well can these decisions be characterized and predicted? This research explores these questions—and others—using both theoretical and experimental means. A value gap model is developed that facilitates simulation based insights to target optimality. The effect of a target is characterized in terms of a value gap that is defined as the difference in value between what was selected based on a target and what the organization would have preferred. A copula based method is used to simulate future decision situations and the expected value gap is calculated as a function of model parameters. Several trends in target optimality are observed that are robust to changes in the probability distributions over future decision alternatives. Specifically, the optimal target (i) decreases as the organization’s risk aversion increases, (ii) increases as the number of available alternatives increase, and (iii) the presence of an efficient frontier of decision alternatives reduces the consequences of setting targets higher than optimal. A behavioral experiment is conducted to compare target-based decision making to decisions in the absence of a target. The results show that while target based decision making can be well predicted based on the properties of the decision alternatives alone. Decisions in the absence of a target, however, cannot be predicted based on the alternatives alone. Information about individualized differences in risk preferences is required to identify trends in the decision making behavior. These results have strong implications for decisions about whether or not to used target based incentives within an organization. The research concludes with an application to engineering systems and a discussion of additional questions raised by the research that point to directions for new research

Efficient Likelihood Evaluation of State-Space Representations

We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted integrands to achieve likelihood approximation. Optimized approximations of targeted integrands are constructed via efficient importance sampling. Resulting likelihood approximations are continuous functions of model parameters, greatly enhancing parameter estimation. We illustrate our procedure in applications to dynamic stochastic general equilibrium models.Adaption, dynamic stochastic general equilibrium model, efficient importance sampling, kernel density approximation, particle filter.

An expected utility theory that matches human performance

Maximising expected utility has long been accepted as a valid model of rational behaviour, however, it "has limited descriptive accuracy sim- ply because, in practice, people do not always behave in the prescribed way. This is considered evidence that either people are not rational, expected utility is not an appropriate characterisation of rationality, or combination of these. This thesis proposes that a modified form of expected utility hypothesis is normative, suggesting how people ought to behave and descriptive of how they actually do behave, provided that: a) most utility has no meaning unless it is in the presence of potential competitors; b) there is uncertainty in the nature of com- petitors; c) statements of probability are associated with uncertainty; d) utility is marginalised over uncertainty, with framing effects pro- viding constraints; and that e) utility is sensitive to risk, which, taken with reward and uncertainty suggests a three dimensional representa- tion. The first part of the thesis investigates the nature of reward in four experiments and proposes that a three dimensional reward struc- ture (reward, risk, and uncertainty) provides a better description of utility than reward alone. It also proposes that the semantic differ- ential, a well researched psychological instrument, is a representation or description of the reward structure. The second part of the thesis provides a mathematical model of a value function and a probabil- ity weighting function, testing them together against extant problem cases for decision making. It is concluded that utility, perhaps more accurately described as advantage in the present case, when construed as three dimensions and the result of a competition, provides a good explanation of many of the problem cases that are documented in the decision making literature.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Measuring and Modeling the Construction of Preferences in Decision Making under Risk

When people are asked whether they like to take risks, their responses are typically consistent over time and predictive of real-world behavior. Hence, risk attitude can be regarded as a stable psychological trait (Frey et al., 2017). Yet, in behavioral risky choice tasks used in psychological and economic research—such as choices between lotteries, abstractly described in terms of outcomes and probabilities—behavior often varies considerably across measurement time-points and formats of the task (Frey et al., 2017; Pedroni et al., 2017). It seems paradoxical that decisions in these situations—which try to condense the problem of decision making under risk to its essential parts—are rarely an expressions of a person’s stable, latent risk attitude. This dissertation examines why experimental risky choice behavior can be notoriously hard to predict, and how the methodological and theoretical apparatus with which we approach the study of risk preferences shapes the inferences we can make. In the first chapter I introduce major theoretical perspectives on decision making under risk and the methods their proponents rely on. The notion of constructed preferences (Lichtenstein & Slovic, 2006; Slovic, 1995) is introduced as a general framework for understanding the lack of temporal stability and convergent validity of behavioral measures of risk attitude. According to this framework, behavioral risk preferences may be constructed on the spot, in the light of available cues and processing capacities. Hence, features of the choice environment—which have nothing to do with risk itself—and psychological characteristics of the decision maker—besides dispositional risk attitude—may profoundly shape the process and output of preference construction. In the subsequent chapters I investigate how surface features of stimulus materials, and individual differences in psychological characteristics, as well as their interplay, shape risky choice behavior. I also use different approaches of computational modeling to describe and explain these changes in risky choice and the underlying cognitive processes. In chapter 2 I demonstrate that in choices between a risky and a safe option, apparent age differences in risk attitude crucially depend on whether the options differ in complexity, rather than on age differences in latent risk attitude. In chapter 3 I investigate whether differences in option complexity also shape (age differences in) tasks used to measure framing effects, loss aversion, and delay discounting. This experiment identifies boundary conditions for the effects of option complexity. In chapter 4 I turn from focusing predominantly on behavior and its dependence on the anatomy of the task towards underlying cognitive processes. I demonstrate that risky choice behavior is shaped by differences between younger and older adults in the ability to implement selective attention. In chapter 5 I demonstrate why it may be useful to view risky choice through the lens of different formal theories—both economic and psychological ones—by identifying systematic signatures of attentional biases simulated in the attentional drift diffusion model in the parameters of cumulative prospect theory. Overall, this dissertation shows why decision making under risk cannot be comprehensively understood in terms of latent risk attitude alone. It identifies specific contextual (option complexity) and psychological (selective attention) determinants of risky choice behavior which need to be taken into account as well, and explains how they affect the underlying process of preference construction, using computational modeling. Moreover, this work underlines the merits of theoretical and methodological pluralism for studying the variable, context-sensitive aspects of risky choice behavior and individual differences therein

Temporal and social discounting of pain and illness

Unhealthy behaviour often entails short-term indulgence at the expense of long-term health. This thesis examines an hypothesis that temporal discounting, a measure of the extent to which a person devalues delayed benefit, predicts unhealthy behaviour. The work also evaluates temporal discounting as a psychological model for unhealthy behaviour, in particular unhealthy behaviour that is enacted in spite of healthy goals, and behaviour with painful consequences. Studies examining relationships between temporal discounting and health behaviour are systematically reviewed, with the finding that discounting of reward correlates with many forms of appetitive unhealthy behaviour. It is proposed that while steep discounting predisposes to unhealthy behaviour, goal-incongruent behaviour is better explained by the interfering effect of prelearned or innate values. Also, conventional discounting fails to account for the fact that many people prefer to expedite inevitable pain or illness. An explanation is that people dislike waiting for pain, termed ‘dread’. The empirical work of this thesis establishes how dread depends on delay, by asking participants to titrate the timing and severity of their own pain or that of others. For the average participant, the dread of pain accumulated at a decreasing rate as pain was delayed. Dread was found to be less marked when deciding on behalf of socially close others. Unexpectedly a tendency to dread future pain in one-off choices did not predict saving a budget of pain relief in sequential choices. Further experiments examined social discounting for pain, finding that participants appear more averse to causing pain in others than in themselves, a tendency that is discounted with social distance. Conclusions are that temporal discounting of reward is a promising marker of appetitive unhealthy behaviour, with a considerable evidence base, while dread offers a candidate marker for engagement in health-promoting behaviour with painful consequences, a possibility which demands further investigation.Open Acces

The attraction effect modulates reward prediction errors and intertemporal choices

Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE)-in the absence of choice-violate the independence of irrelevant alternatives principle. We first demonstrate that intertemporal decision making is prone to the attraction effect in humans. In an independent group of participants, we then investigate how this affects the neural and behavioral valuation of outcomes, using a novel intertemporal lottery task and fMRI. Participants' behavioral responses (i.e., satisfaction ratings) were systematically modulated by the attraction effect, and this modulation was correlated across participants with the respective change of the RPE signal in the Nucleus Accumbens. Furthermore, we show that since exponential and hyperbolic discounting models are unable to account for the attraction effect, recently proposed sequential sampling models might be more appropriate to describe intertemporal choices. Our findings demonstrate for the first time that the attraction effect modulates subjective valuation even in the absence of choice. The findings also challenge the prospect of using neuroscientific methods to measure utility in a context-free manner and have important implications for theories of reinforcement learning and delay discounting.; Many theories of value-based decision making assume that people first assess the attractiveness of each option independently of each other and then pick the option with the highest subjective value. The attraction effect, however, shows that adding a new option to a choice set can change the relative value of the existing options, which is a violation of the independence principle. Using an intertemporal choice framework, we test whether such violations also occur when the brain encodes the difference between expected and received rewards (i.e., the reward prediction error). Our results suggest that both intertemporal choice and valuation without choice do not adhere to the independence principle

Multi-attribute decision by sampling : an account of the attraction, compromise and similarity effects

Consumers’ choices are typically influenced by choice context in ways that standard models cannot explain. We provide a concise explanation of the attraction, compromise and similarity effects. Value is assumed to be determined by simple dominance relations between choice options and sampled comparators, and selection of comparators is assumed to be systematically influenced by the choice options. In one experiment, participants viewed differing selections of market options prior to choice. The classic context effects appeared and disappeared as predicted. In the second experiment, individuals’ sampling distributions of market options were influenced by the choice set as predicted by the model

Information integration in perceptual and value-based decisions

Research on the psychology and neuroscience of simple, evidence-based choices has led to an impressive progress in capturing the underlying mental processes as optimal mechanisms that make the fastest decision for a specified accuracy. The idea that decision-making is an optimal process stands in contrast with findings in more complex, motivation-based decisions, focussed on multiple goals with trade-offs. Here, a number of paradoxical and puzzling choice behaviours have been revealed, posing a serious challenge to the development of a unified theory of choice. These choice anomalies have been traditionally attributed to oddities at the representation of values and little is known about the role of the process under which information is integrated towards a decision. In a series of experiments, by controlling the temporal distribution of the decision-relevant information (i.e., sensory evidence or value), I demonstrate that the characteristics of this process cause many puzzling choice paradoxes, such as temporal, risk and framing biases, as well as preference reversal. In Chapter 3, I show that information integration is characterized by temporal biases (Experimental Studies 1-2, Computational Studies 1-3). In Chapter 4, I examine the way the integration process is affected by the immediate decision context (Experimental Studies 3-4, Computational Study 4), demonstrating that prior to integration, the momentary ranking of a sample modifies its magnitude. This principle is further scrutinized in Chapter 5, where a rank-dependent accumulation model is developed (Computational Study 5). The rank-dependent model is shown to underlie preference reversal in multi-attribute choice problems and to predict that choice is sensitive, not only to the mean strength of the information, but also to its variance, favouring riskier options (Computational Study 6). This prediction is further confirmed in Chapter 6, in a number of experiments (Experimental Studies 5-7) while the direction of risk preferences is found to be modulated by the cognitive perspective induced by the task framing (Experimental Study 8). I conclude that choice arises from a deliberative process which gathers samples of decision-relevant information, weighs them according to their salience and subsequently accumulates them. The salience of a sample is determined by i) its temporal order and ii) its local ranking in the decision context, while the direction of the weighting is controlled by the task framing. The implications of this simple, microprocess model are discussed with respect to choice optimality while directions for future research, towards the development of a unified theory of choice, are suggested