Exploring the scope of neurometrically informed mechanism design

A basic goal in mechanism design is to construct mechanisms that simultaneously satisfy efficiency, voluntary participation, and dominant strategy incentive compatibility. Previous work has shown that this is impossible, unless the agents and planner have sufficient information about each other and common knowledge. These results have remained largely theoretical because the required information is generally not available in practical applications. However, recent work has shown that these limitations can be overcome in simple settings, using neurometric technologies that provide noisy signals of subjects' preferences that can be used in the mechanism design problem. Here we build on this work by carrying out two new experiments designed to test the extent to which these Neurometrically Informed Mechanisms (NIMs) can be applied to more complicated and realistic environments. We find robustness to large type and action space and to the degrees of loss and risk-aversion observed in most of our sample

Economic Games Quantify Diminished Sense of Guilt in Patients with Damage to the Prefrontal Cortex

Damage to the ventromedial prefrontal cortex (VMPFC) impairs concern for other people, as reflected in the dysfunctional real-life social behavior of patients with such damage, as well as their abnormal performances on tasks ranging from moral judgment to economic games. Despite these convergent data, we lack a formal model of how, and to what degree, VMPFC lesions affect an individual's social decision-making. Here we provide a quantification of these effects using a formal economic model of choice that incorporates terms for the disutility of unequal payoffs, with parameters that index behaviors normally evoked by guilt and envy. Six patients with focal VMPFC lesions participated in a battery of economic games that measured concern about payoffs to themselves and to others: dictator, ultimatum, and trust games. We analyzed each task individually, but also derived estimates of the guilt and envy parameters from aggregate behavior across all of the tasks. Compared with control subjects, the patients donated significantly less and were less trustworthy, and overall our model found a significant insensitivity to guilt. Despite these abnormalities, the patients had normal expectations about what other people would do, and they also did not simply generate behavior that was more noisy. Instead, the findings argue for a specific insensitivity to guilt, an abnormality that we suggest characterizes a key contribution made by the VMPFC to social behavior

Uncovering the computational mechanisms underlying many-alternative choice

How do we choose when confronted with many alternatives? There is surprisingly little decision modelling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally provides slightly better fits to choices and response times, while the gaze-driven evidence accumulation and comparison model provides the best overall account of the data when also considering the empirical relation between gaze allocation and choice

Measuring utility with diffusion models

The diffusion model (DDM) is a prominent account of how people make decisions. Many of these decisions involve comparing two alternatives based on differences of perceived stimulus magnitudes, such as economic values. Here, we propose a consistent estimator for the parameters of a DDM in such cases. This estimator allows us to derive decision thresholds, drift rates, and subjective percepts (i.e., utilities in economic choice) directly from the experimental data. This eliminates the need to measure these values separately or to assume specific functional forms for them. Our method also allows one to predict drift rates for comparisons that did not occur in the dataset. We apply the method to two datasets, one comparing probabilities of earning a fixed reward and one comparing objects of variable reward value. Our analysis indicates that both datasets conform well to the DDM. Interestingly, we find that utilities are linear in probability and slightly convex in reward

Special Issue of the Journal of Behavioral Decision Making on “Applications and Innovations of Eye‐Movement Research in Judgment and Decision Making”

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100158/1/bdm1798.pd

The Wick in the Candle of Learning: Epistemic Curiosity Activates Reward Circuitry and Enhances Memory

Curiosity has been described as a desire for learning and knowledge, but its underlying mechanisms are not well understood. We scanned subjects with functional magnetic resonance imaging while they read trivia questions. The level of curiosity when reading questions was correlated with activity in caudate regions previously suggested to be involved in anticipated reward. This finding led to a behavioral study, which showed that subjects spent more scarce resources (either limited tokens or waiting time) to find out answers when they were more curious. The functional imaging also showed that curiosity increased activity in memory areas when subjects guessed incorrectly, which suggests that curiosity may enhance memory for surprising new information. This prediction about memory enhancement was confirmed in a behavioral study: Higher curiosity in an initial session was correlated with better recall of surprising answers 1 to 2 weeks later

Correspondence: Are Cognitive Functions Localizable? Colin Camerer et al. versus Marieke van Rooij and John G. Holden

The Fall 2011 issue of this journal published a two-paper section on “Neuroeconomics.” One paper, by Ernst Fehr and Antonio Rangel, clearly and concisely summarized a small part of the fast-growing literature. The second paper, “It’s about Space, It’s about Time, Neuroeconomics, and the Brain Sublime,” by Marieke van Rooij and Guy Van Orden, is beautifully written and enjoyable to read, but misleading in many critical ways. A number of economists and neuroscientists working at the intersection of the two fields shared our reaction and have signed this letter, as shown below. Some of the paper’s descriptions of empirical findings and methods in neuroeconomics are incomplete, badly out of date, or flatly wrong. In studies the authors describe in detail, their skeptical interpretations have often been refuted by published data, old and new, that they overlook

Neurometrically Informed Mechanism Design and the Role of Visual Fixations in Simple Choice

The young field of neuroeconomics has already produced many important insights into the neurobiological underpinnings of decision making. However, at this early stage it is still unclear how much influence the field will have on mainstream economics. Here, I show how a neuroeconomics approach can shed light on two classic economic problems. First, I show that it is possible to predict individuals’ values for public goods, using functional magnetic resonance imaging (fMRI)-based pattern classification. With such predictions in hand, I demonstrate that it is possible to solve the free-rider problem, by taxing individuals based both on the values that they themselves report and on the predicted values (using fMRI). I go on to more generally prove that by using any informative signal of value, it is possible to overcome classic impossibility results in mechanism design. This allows us to construct mechanisms that simultaneously satisfy dominant strategy incentive compatibility, voluntary participation, budget-balance and social efficiency. Such mechanisms were previously thought to be impossible. I demonstrate how to construct such mechanisms, and test them in three different public goods experiments. Second, I show that individuals’ looking patterns are critical to the decision making process. When people make choices between options, they tend to look back and forth between them. One might think that these “fixations” are an unimportant by-product of the choice process, but I demonstrate that they are in fact intimately tied to the comparison process. By using a variant of the drift-diffusion models from the perceptual decision making literature, I find that fixations seem to bias the accumulation of evidence towards the item that is being looked at. Therefore, if one spends more time looking at one item over the other, then one is more likely to choose that item. Critically, I am able to show that this effect is not due to subjects looking longer at preferred items. The model has deep implications for how looking patterns (treated as exogenous) should bias choices, and I confirm these predictions using eye-tracking data from subjects choosing between snack foods

Multi-parameter utility and drift-rate functions conflate attribute weights and choice consistency

Standard decision models include two components: subjective-value (utility) functions and stochastic choice rules. The first establishes the relative weighting of the attributes or dimensions and the second determines how consistently the higher utility option is chosen. For a decision problem with M attributes, researchers often estimate M-1 utility parameters and separately estimate a choice-consistency parameter. Instead, researchers sometimes estimate M parameters in the utility function and neglect choice consistency. I argue that while these two approaches are mathematically identical, the latter conflates utility and consistency parameters, leading to ambiguous interpretations and conclusions. At the same time, behavior arises from the interaction of utility and consistency parameters, so for choice prediction they should not be considered in isolation. Overall, I advocate for a clear separation between utility functions and stochastic choice rules when modeling decision-making, and reinforce the notion that researchers should use M-1 parameters for M-attribute decision problems