ORCID Profile
0000-0003-2308-2603
Current Organisation
University of Cambridge
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Experimental Economics | Banking, Finance and Investment | Financial Institutions (incl. Banking) | Applied Economics
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in Commerce, Management, Tourism and Services | Expanding Knowledge in the Information and Computing Sciences | Finance Services | Investment Services (excl. Superannuation) |
Related Links
Publications
A Behavioral and Neural Evaluation of Prospective Decision-Making under Risk
Publisher: Society for Neuroscience
Date: 27-10-2010
DOI: 10.1523/JNEUROSCI.1459-10.2010
Abstract: Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes.
Neural Antecedents of Financial Decisions: Figure 1.
Publisher: Society for Neuroscience
Date: 08-2007
DOI: 10.1523/JNEUROSCI.1564-07.2007
Abstract: To explain investing decisions, financial theorists invoke two opposing metrics: expected reward and risk. Recent advances in the spatial and temporal resolution of brain imaging techniques enable investigators to visualize changes in neural activation before financial decisions. Research using these methods indicates that although the ventral striatum plays a role in representation of expected reward, the insula may play a more prominent role in the representation of expected risk. Accumulating evidence also suggests that antecedent neural activation in these regions can be used to predict upcoming financial decisions. These findings have implications for predicting choices and for building a physiologically constrained theory of decision-making.
Local parametric analysis of hedging in discrete time
Publisher: Elsevier BV
Date: 11-1997
Adding Prediction Risk to the Theory of Reward Learning
Publisher: Wiley
Date: 05-2007
Abstract: This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement learning. It argues that prediction risk is most effectively incorporated by scaling the prediction errors. This way, the learning rate needs adjusting only when the covariance between optimal predictions and past (scaled) prediction errors changes. Evidence is discussed that suggests that the dopaminergic system in the (human and nonhuman) primate brain encodes prediction risk, and that prediction errors are indeed scaled with prediction risk (adaptive encoding).
Ambiguity in Asset Markets: Theory and Experiment
Publisher: Oxford University Press (OUP)
Date: 05-01-2010
DOI: 10.1093/RFS/HHP106
Decision Neuroscience: Why We Become More Cautious with Age
Publisher: Elsevier BV
Date: 06-2016
DOI: 10.1016/J.CUB.2016.04.061
Abstract: A recent study suggests that risk-taking decreases with age and that this may be related to dopamine-modulated changes in Pavlovian approach behavior, and not a reduction in the subjective value of incremental rewards as traditional models from economics and psychology would have claimed.
Excess demand and equilibration in multi-security financial markets: the empirical evidence
Publisher: Elsevier BV
Date: 2003
Decision Making: How the Brain Weighs the Evidence
Publisher: Elsevier BV
Date: 09-2012
DOI: 10.1016/J.CUB.2012.07.031
Abstract: The brain has to weigh incoming sensory evidence against prior beliefs, the relative weight given to each depending on the relative uncertainties. Neuroscience now shows how the human brain accomplishes this.
Uncertainty and computational complexity.
Publisher: The Royal Society
Date: 31-12-2019
Abstract: Modern theories of decision-making typically model uncertainty about decision options using the tools of probability theory. This is exemplified by the Savage framework, the most popular framework in decision-making research. There, decision-makers are assumed to choose from among available decision options as if they maximized subjective expected utility, which is given by the utilities of outcomes in different states weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using Bayes’ Law. The primary concern of the Savage framework is to ensure that decision-makers’ choices are rational . Here, we use concepts from computational complexity theory to expose two major weaknesses of the framework. Firstly, we argue that in most situations, subjective utility maximization is computationally intractable, which means that the Savage axioms are implausible. We discuss empirical evidence supporting this claim. Secondly, we argue that there exist many decision situations in which the nature of uncertainty is such that (random) s ling in combination with Bayes’ Law is an ineffective strategy to reduce uncertainty. We discuss several implications of these weaknesses from both an empirical and a normative perspective. This article is part of the theme issue ‘Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications’.
The chronometry of risk processing in the human cortex
Publisher: Frontiers Media SA
Date: 2013
How Humans Solve Complex Problems: The Case of the Knapsack Problem
Publisher: Springer Science and Business Media LLC
Date: 07-10-2016
DOI: 10.1038/SREP34851
Abstract: Life presents us with problems of varying complexity. Yet, complexity is not accounted for in theories of human decision-making. Here we study instances of the knapsack problem, a discrete optimisation problem commonly encountered at all levels of cognition, from attention gating to intellectual discovery. Complexity of this problem is well understood from the perspective of a mechanical device like a computer. We show experimentally that human performance too decreased with complexity as defined in computer science. Defying traditional economic principles, participants spent effort way beyond the point where marginal gain was positive, and economic performance increased with instance difficulty. Human attempts at solving the instances exhibited commonalities with algorithms developed for computers, although biological resource constraints–limited working and episodic memories–had noticeable impact. Consistent with the very nature of the knapsack problem, only a minority of participants found the solution–often quickly–but the ones who did appeared not to realise. Substantial heterogeneity emerged, suggesting why prizes and patents, schemes that incentivise intellectual discovery but discourage information sharing, have been found to be less effective than mechanisms that reveal private information, such as markets.
The Neurobiological Foundations of Valuation in Human Decision Making Under Uncertainty
Publisher: Elsevier
Date: 2009
Explicit neural signals reflecting reward uncertainty
Publisher: The Royal Society
Date: 10-2008
Abstract: The acknowledged importance of uncertainty in economic decision making has stimulated the search for neural signals that could influence learning and inform decision mechanisms. Current views distinguish two forms of uncertainty, namely risk and ambiguity, depending on whether the probability distributions of outcomes are known or unknown. Behavioural neurophysiological studies on dopamine neurons revealed a risk signal, which covaried with the standard deviation or variance of the magnitude of juice rewards and occurred separately from reward value coding. Human imaging studies identified similarly distinct risk signals for monetary rewards in the striatum and orbitofrontal cortex (OFC), thus fulfilling a requirement for the mean variance approach of economic decision theory. The orbitofrontal risk signal covaried with in idual risk attitudes, possibly explaining in idual differences in risk perception and risky decision making. Ambiguous gambles with incomplete probabilistic information induced stronger brain signals than risky gambles in OFC and amygdala, suggesting that the brain's reward system signals the partial lack of information. The brain can use the uncertainty signals to assess the uncertainty of rewards, influence learning, modulate the value of uncertain rewards and make appropriate behavioural choices between only partly known options.
Inducing liquidity in thin financial markets through combined-value trading mechanisms
Publisher: Elsevier BV
Date: 10-2002
Positive Temporal Dependence of the Biological Clock Implies Hyperbolic Discounting
Publisher: Frontiers Media SA
Date: 2011
Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism
Publisher: Public Library of Science (PLoS)
Date: 23-10-2015
Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings
Publisher: Public Library of Science (PLoS)
Date: 20-01-2011
Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Task
Publisher: Public Library of Science (PLoS)
Date: 31-01-2013
Neural Mechanisms Behind Identification of Leptokurtic Noise and Adaptive Behavioral Response
Publisher: Oxford University Press (OUP)
Date: 04-02-2016
Market Microstructure Effects of Government Intervention in the Foreign Exchange Market
Publisher: Oxford University Press (OUP)
Date: 07-1991
DOI: 10.1093/RFS/4.3.513
Equilibrium Asset Pricing and Portfolio Choice Under Asymmetric Information
Publisher: Oxford University Press (OUP)
Date: 18-01-2010
DOI: 10.1093/RFS/HHP113
Modelling price pressure in financial markets
Publisher: Elsevier BV
Date: 10-2009
A TEST OF A GENERAL EQUILIBRIUM STOCK OPTION PRICING MODEL
Publisher: Wiley
Date: 10-1993
Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning
Publisher: Public Library of Science (PLoS)
Date: 21-02-2013
Formalizing the Function of Anterior Insula in Rapid Adaptation
Publisher: Frontiers Media SA
Date: 04-12-2018
Estimating self-performance when making complex decisions
Publisher: Center for Open Science
Date: 06-04-2023
Abstract: Metacognition, the ability to monitor and reflect on our own mental states, enables us to assess our performance at different levels – from confidence in in idual decisions to overall self-performance estimates (SPEs). It plays a particularly important part in computationally complex decisions that require a high level of cognitive resources, as the allocation of such limited resources presumably is based on metacognitive evaluations. However, little is known about metacognition in complex decisions, in particular, how people construct SPEs. Here, we examined how SPEs are modulated by task difficulty and feedback in cognitively complex economic decision-making, with reference to simple perceptual decision-making. We found that, in both types of decision-making, participants’ objective performance was only affected by task difficulty but not by the presence of feedback. In complex economic decision-making, participants had lower SPEs in the absence of feedback (compared to the presence of feedback) in easy trials only but not in hard trials, while in simple perceptual decision-making, SPEs were lower in the absence of feedback in both easy and hard trials. Our findings suggest that people estimate their performance in complex economic decision-making through distinct metacognitive mechanisms for easy and hard instances.
The Econometrics of Learning in Financial Markets
Publisher: Cambridge University Press (CUP)
Date: 02-1995
DOI: 10.1017/S0266466600009075
Abstract: The asymptotic behavior of the s le paths of two popular statistics that test market efficiency are investigated when markets learn to have rational expectations. Two cases are investigated, where, should markets start out at a rational expectations equilibrium, both statistics would asymptotically generate standard Brownian motions. In a first case, where agents are Bayesian and payoffs exogenous, the statistics have identical s le paths, but they are not standard Brownian motions. Whereas the finite-dimensional distributions are Gaussian, there may be a bias if agents' initial beliefs differ. A second case is considered, where payoffs are in part endogenous, yet agents consider them to be drawn from a stationary, exogenous distribution, which they attempt to learn in a frequentist way. In that case, one statistic behaves as if the economy were at a rational expectations equilibrium from the beginning on. The other statistic has s le paths with substantially non-Gaussian finite-dimensional distributions. Moreover, there is a negative bias. The behavior of the two statistics in the second case matches remarkably well the empirical results in an investigation of the prices of six foreign currency contracts over the period 1973–1990.
MAOA-L carriers are better at making optimal financial decisions under risk
Publisher: The Royal Society
Date: 08-12-2010
Abstract: Genes can affect behaviour towards risks through at least two distinct neurocomputational mechanisms: they may affect the value assigned to different risky options, or they may affect the way in which the brain adjudicates between options based on their value. We combined methods from neuroeconomics and behavioural genetics to investigate the impact that the genes encoding for monoamine oxidase-A (MAOA), the serotonin transporter (5-HTT) and the dopamine D4 receptor (DRD4) have on these two computations. Consistent with previous literature, we found that carriers of the MAOA-L polymorphism were more likely to take financial risks. Our computational choice model, rooted in established decision theory, showed that MAOA-L carriers exhibited such behaviour because they are able to make better financial decisions under risk, and not because they are more impulsive. In contrast, we found no behavioural or computational differences among the 5-HTT and DRD4 polymorphisms.
Asset Prices and Trading Volume in a Beauty Contest
Publisher: Oxford University Press (OUP)
Date: 04-1998
An Optimal IPO Mechanism
Publisher: Oxford University Press (OUP)
Date: 2002
Neural computations underlying inverse reinforcement learning in the human brain
Publisher: eLife Sciences Publications, Ltd
Date: 30-10-2017
DOI: 10.7554/ELIFE.29718
Abstract: In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process orceable from the values and concerns of the observer him/herself.
Chimpanzee choice rates in competitive games match equilibrium game theory predictions
Publisher: Springer Science and Business Media LLC
Date: 05-06-2014
DOI: 10.1038/SREP05182
Abstract: The capacity for strategic thinking about the payoff-relevant actions of conspecifics is not well understood across species. We use game theory to make predictions about choices and temporal dynamics in three abstract competitive situations with chimpanzee participants. Frequencies of chimpanzee choices are extremely close to equilibrium (accurate-guessing) predictions and shift as payoffs change, just as equilibrium theory predicts. The chimpanzee choices are also closer to the equilibrium prediction and more responsive to past history and payoff changes, than two s les of human choices from experiments in which humans were also initially uninformed about opponent payoffs and could not communicate verbally. The results are consistent with a tentative interpretation of game theory as explaining evolved behavior, with the additional hypothesis that chimpanzees may retain or practice a specialized capacity to adjust strategy choice during competition to perform at least as well as, or better than, humans have.
Predicting risk in a multiple stimulus-reward environment
Publisher: Elsevier
Date: 2009
Computational Complexity and Human Decision-Making.
Publisher: Elsevier BV
Date: 12-2017
DOI: 10.1016/J.TICS.2017.09.005
Abstract: The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology.
Neural Correlates of Value, Risk, and Risk Aversion Contributing to Decision Making under Risk
Publisher: Society for Neuroscience
Date: 07-10-2009
DOI: 10.1523/JNEUROSCI.2614-09.2009
Abstract: Decision making under risk is central to human behavior. Economic decision theory suggests that value, risk, and risk aversion influence choice behavior. Although previous studies identified neural correlates of decision parameters, the contribution of these correlates to actual choices is unknown. In two different experiments, participants chose between risky and safe options. We identified discrete blood oxygen level-dependent (BOLD) correlates of value and risk in the ventral striatum and anterior cingulate, respectively. Notably, increasing inferior frontal gyrus activity to low risk and safe options correlated with higher risk aversion. Importantly, the combination of these BOLD responses effectively decoded the behavioral choice. Striatal value and cingulate risk responses increased the probability of a risky choice, whereas inferior frontal gyrus responses showed the inverse relationship. These findings suggest that the BOLD correlates of decision factors are appropriate for an ideal observer to detect behavioral choices. More generally, these biological data contribute to the validity of the theoretical decision parameters for actual decisions under risk.
Differentiable contributions of human amygdalar subregions in the computations underlying reward and avoidance learning
Publisher: Wiley
Date: 03-05-2011
DOI: 10.1111/J.1460-9568.2011.07686.X
Abstract: To understand how the human amygdala contributes to associative learning, it is necessary to differentiate the contributions of its subregions. However, major limitations in the techniques used for the acquisition and analysis of functional magnetic resonance imaging (fMRI) data have hitherto precluded segregation of function with the amygdala in humans. Here, we used high-resolution fMRI in combination with a region-of-interest-based normalization method to differentiate functionally the contributions of distinct subregions within the human amygdala during two different types of instrumental conditioning: reward and avoidance learning. Through the application of a computational-model-based analysis, we found evidence for a dissociation between the contributions of the basolateral and centromedial complexes in the representation of specific computational signals during learning, with the basolateral complex contributing more to reward learning, and the centromedial complex more to avoidance learning. These results provide unique insights into the computations being implemented within fine-grained amygdala circuits in the human brain.
Is Academic Finance Really that Wrong?
Publisher: Elsevier BV
Date: 2017
DOI: 10.2139/SSRN.3122143
Do not Bet on the Unknown Versus Try to Find Out More: Estimation Uncertainty and “Unexpected Uncertainty” Both Modulate Exploration
Publisher: Frontiers Media SA
Date: 2012
Experiments with Financial Markets: Implications for Asset Pricing Theory
Publisher: Cambridge University Press
Date: 06-09-2004
Basic Principles of Asset Pricing Theory: Evidence from Large-Scale Experimental Financial Markets *
Publisher: Oxford University Press (OUP)
Date: 06-2004
The Affective Impact of Financial Skewness on Neural Activity and Choice
Publisher: Public Library of Science (PLoS)
Date: 15-02-2011
Chapter 42 Asset Pricing
Publisher: Elsevier
Date: 2008
Excessive Volatility is Also a Feature of Individual Level Forecasts
Publisher: Informa UK Limited
Date: 02-01-2014
Economic Choices Reveal Probability Distortion in Macaque Monkeys
Publisher: Society for Neuroscience
Date: 18-02-2015
DOI: 10.1523/JNEUROSCI.3653-14.2015
Abstract: Economic choices are largely determined by two principal elements, reward value (utility) and probability. Although nonlinear utility functions have been acknowledged for centuries, nonlinear probability weighting (probability distortion) was only recently recognized as a ubiquitous aspect of real-world choice behavior. Even when outcome probabilities are known and acknowledged, human decision makers often overweight low probability outcomes and underweight high probability outcomes. Whereas recent studies measured utility functions and their corresponding neural correlates in monkeys, it is not known whether monkeys distort probability in a manner similar to humans. Therefore, we investigated economic choices in macaque monkeys for evidence of probability distortion. We trained two monkeys to predict reward from probabilistic gambles with constant outcome values (0.5 ml or nothing). The probability of winning was conveyed using explicit visual cues (sector stimuli). Choices between the gambles revealed that the monkeys used the explicit probability information to make meaningful decisions. Using these cues, we measured probability distortion from choices between the gambles and safe rewards. Parametric modeling of the choices revealed classic probability weighting functions with inverted-S shape. Therefore, the animals overweighted low probability rewards and underweighted high probability rewards. Empirical investigation of the behavior verified that the choices were best explained by a combination of nonlinear value and nonlinear probability distortion. Together, these results suggest that probability distortion may reflect evolutionarily preserved neuronal processing.
Emotional Engagement and Trading Performance
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 11-10-2023
Exploring the Nature of “Trader Intuition”
Publisher: Wiley
Date: 21-09-2010
Using Neural Data to Test a Theory of Investor Behavior: An Application to Realization Utility
Publisher: Wiley
Date: 17-03-2014
DOI: 10.1111/JOFI.12126
IPO Post-Issue Markets: Questionable Predilections But Diligent Learners?
Publisher: MIT Press - Journals
Date: 05-2001
Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk
Publisher: MDPI AG
Date: 26-10-2020
DOI: 10.3390/RISKS8040113
Abstract: In traditional Reinforcement Learning (RL), agents learn to optimize actions in a dynamic context based on recursive estimation of expected values. We show that this form of machine learning fails when rewards (returns) are affected by tail risk, i.e., leptokurtosis. Here, we adapt a recent extension of RL, called distributional RL (disRL), and introduce estimation efficiency, while properly adjusting for differential impact of outliers on the two terms of the RL prediction error in the updating equations. We show that the resulting “efficient distributional RL” (e-disRL) learns much faster, and is robust once it settles on a policy. Our paper also provides a brief, nontechnical overview of machine learning, focusing on RL.
From behavioural economics to neuroeconomics to decision neuroscience: The ascent of biology in research on human decision making
Publisher: Elsevier BV
Date: 10-2015
The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs
Publisher: Society for Neuroscience
Date: 26-06-2013
Expectations and learning in Iowa
Publisher: Elsevier BV
Date: 09-2000
The CAPM in thin experimental financial markets
Publisher: Elsevier BV
Date: 07-2002
A General Equilibrium Model of Changing Risk Premia: Theory and Tests
Publisher: Oxford University Press (OUP)
Date: 10-1989
DOI: 10.1093/RFS/2.4.467
Encoding of Marginal Utility across Time in the Human Brain
Publisher: Society for Neuroscience
Date: 29-07-2009
DOI: 10.1523/JNEUROSCI.1126-09.2009
Abstract: Marginal utility theory prescribes the relationship between the objective property of the magnitude of rewards and their subjective value. Despite its pervasive influence, however, there is remarkably little direct empirical evidence for such a theory of value, let alone of its neurobiological basis. We show that human preferences in an intertemporal choice task are best described by a model that integrates marginally diminishing utility with temporal discounting. Using functional magnetic resonance imaging, we show that activity in the dorsal striatum encodes both the marginal utility of rewards, over and above that which can be described by their magnitude alone, and the discounting associated with increasing time. In addition, our data show that dorsal striatum may be involved in integrating subjective valuation systems inherent to time and magnitude, thereby providing an overall metric of value used to guide choice behavior. Furthermore, during choice, we show that anterior cingulate activity correlates with the degree of difficulty associated with dissonance between value and time. Our data support an integrative architecture for decision making, revealing the neural representation of distinct subcomponents of value that may contribute to impulsivity and decisiveness.
What Decision Neuroscience Teaches Us About Financial Decision Making
Publisher: Annual Reviews
Date: 12-2009
DOI: 10.1146/ANNUREV.FINANCIAL.102708.141514
Abstract: Financial decision making is the outcome of complex neurophysiological processes involving, among others, constant re-evaluation of the statistics of the problem at hand, balancing of the various emotional aspects, and computation of the very value signals that are at the core of modern economic thinking. The evidence suggests that emotions play a crucial supporting role in the mathematical computations needed for reasoned choice, rather than interfering with it, even if emotions (and their mathematical counterparts) may not always be balanced appropriately. Decision neuroscience can be expected in the near future to provide a number of effective tools for improved financial decision making.
Prices and Portfolio Choices in Financial Markets: Theory, Econometrics, Experiments
Publisher: The Econometric Society
Date: 07-2007
The human prefrontal cortex mediates integration of potential causes behind observed outcomes
Publisher: American Physiological Society
Date: 09-2011
Abstract: Prefrontal cortex has long been implicated in tasks involving higher order inference in which decisions must be rendered, not only about which stimulus is currently rewarded, but also which stimulus dimensions are currently relevant. However, the precise computational mechanisms used to solve such tasks have remained unclear. We scanned human participants with functional MRI, while they performed a hierarchical intradimensional/extradimensional shift task to investigate what strategy subjects use while solving higher order decision problems. By using a computational model-based analysis, we found behavioral and neural evidence that humans solve such problems not by occasionally shifting focus from one to the other dimension, but by considering multiple explanations simultaneously. Activity in human prefrontal cortex was better accounted for by a model that integrates over all available evidences than by a model in which attention is selectively gated. Importantly, our model provides an explanation for how the brain determines integration weights, according to which it could distribute its attention. Our results demonstrate that, at the point of choice, the human brain and the prefrontal cortex in particular are capable of a weighted integration of information across multiple evidences.
Risk and risk prediction error signals in anterior insula
Publisher: Springer Science and Business Media LLC
Date: 29-05-2010
DOI: 10.1007/S00429-010-0253-1
Abstract: Most accounts of the function of anterior insula in the human brain refer to concepts that are difficult to formalize, such as feelings and awareness. The discovery of signals that reflect risk assessment and risk learning, however, opens the door to formal analysis. Hitherto, activations have been correlated with objective versions of risk and risk prediction error, but subjective versions (influenced by pessimism/optimism or risk aversion/tolerance) exist. Activation in closely related cortical structures has been found to be both objective (anterior cingulate cortex) and subjective (inferior frontal gyrus). For this quantitative analysis of uncertainty-induced neuronal activation to further understanding of insula's role in feelings and awareness, however, formalization and documentation of the relation between uncertainty and feelings/awareness will be needed. One obvious starting point is the link with failure anxiety and error awareness.
Not so smart? “Smart” drugs increase the level but decrease the quality of cognitive effort
Publisher: American Association for the Advancement of Science (AAAS)
Date: 16-06-2023
Abstract: The efficacy of pharmaceutical cognitive enhancers in everyday complex tasks remains to be established. Using the knapsack optimization problem as a stylized representation of difficulty in tasks encountered in daily life, we discover that methylphenidate, dextro hetamine, and modafinil cause knapsack value attained in the task to diminish significantly compared to placebo, even if the chance of finding the optimal solution (~50%) is not reduced significantly. Effort (decision time and number of steps taken to find a solution) increases significantly, but productivity (quality of effort) decreases significantly. At the same time, productivity differences across participants decrease, even reverse, to the extent that above-average performers end up below average and vice versa. The latter can be attributed to increased randomness of solution strategies. Our findings suggest that “smart drugs” increase motivation, but a reduction in quality of effort, crucial to solve complex problems, annuls this effect.
Human Insula Activation Reflects Risk Prediction Errors As Well As Risk
Publisher: Society for Neuroscience
Date: 12-03-2008
DOI: 10.1523/JNEUROSCI.4286-07.2008
Abstract: Understanding how organisms deal with probabilistic stimulus-reward associations has been advanced by a convergence between reinforcement learning models and primate physiology, which demonstrated that the brain encodes a reward prediction error signal. However, organisms must also predict the level of risk associated with reward forecasts, monitor the errors in those risk predictions, and update these in light of new information. Risk prediction serves a dual purpose: (1) to guide choice in risk-sensitive organisms and (2) to modulate learning of uncertain rewards. To date, it is not known whether or how the brain accomplishes risk prediction. Using functional imaging during a simple gambling task in which we constantly changed risk, we show that an early-onset activation in the human insula correlates significantly with risk prediction error and that its time course is consistent with a role in rapid updating. Additionally, we show that activation previously associated with general uncertainty emerges with a delay consistent with a role in risk prediction. The activations correlating with risk prediction and risk prediction errors are the analogy for risk of activations correlating with reward prediction and reward prediction errors for reward expectation. As such, our findings indicate that our understanding of the neural basis of reward anticipation under uncertainty needs to be expanded to include risk prediction.
Neurobiological studies of risk assessment: A comparison of expected utility and mean-variance approaches
Publisher: Springer Science and Business Media LLC
Date: 12-2008
DOI: 10.3758/CABN.8.4.363
Perception of intentionality in investor attitudes towards financial risks
Publisher: Elsevier BV
Date: 09-2019
In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles
Publisher: Elsevier BV
Date: 09-2013
The Impact of Disappointment in Decision Making: Inter-Individual Differences and Electrical Neuroimaging
Publisher: Frontiers Media SA
Date: 2011
The Role of the Ventromedial Prefrontal Cortex in Abstract State-Based Inference during Decision Making in Humans
Publisher: Society for Neuroscience
Date: 09-08-2006
DOI: 10.1523/JNEUROSCI.1010-06.2006
Abstract: Many real-life decision-making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as the ventromedial prefrontal cortex (vmPFC), use a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure as in standard reinforcement learning. To discriminate between these possibilities, we scanned human subjects with functional magnetic resonance imaging while they performed a simple decision-making task with higher-order structure, probabilistic reversal learning. We found that neural activity in a key decision-making region, the vmPFC, was more consistent with a computational model that exploits higher-order structure than with simple reinforcement learning. These results suggest that brain regions, such as the vmPFC, use an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.
In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles
Publisher: Elsevier BV
Date: 11-2013
Risk aversion in laboratory asset markets
Publisher: Emerald (MCB UP )
Date: 2008
Lectures on Corporate Finance
Publisher: WORLD SCIENTIFIC
Date: 10-2006
DOI: 10.1142/6188
Neural correlates of mentalizing-related computations during strategic interactions in humans
Publisher: Proceedings of the National Academy of Sciences
Date: 06-05-2008
Abstract: Competing successfully against an intelligent adversary requires the ability to mentalize an opponent's state of mind to anticipate his/her future behavior. Although much is known about what brain regions are activated during mentalizing, the question of how this function is implemented has received little attention to date. Here we formulated a computational model describing the capacity to mentalize in games. We scanned human subjects with functional MRI while they participated in a simple two-player strategy game and correlated our model against the functional MRI data. Different model components captured activity in distinct parts of the mentalizing network. While medial prefrontal cortex tracked an in idual's expectations given the degree of model-predicted influence, posterior superior temporal sulcus was found to correspond to an influence update signal, capturing the difference between expected and actual influence exerted. These results suggest dissociable contributions of different parts of the mentalizing network to the computations underlying higher-order strategizing in humans.
Competition in Portfolio Management: Theory and Experiment
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 08-2015
Abstract: We explore theoretically and experimentally the general equilibrium price and allocation implications of delegated portfolio management when the investor–manager relationship is nonexclusive. Our theory predicts that competition forces managers to promise portfolios that mimic Arrow–Debreu (AD) securities, which investors then combine to fit their preferences. A weak version of the capital asset pricing model (CAPM) obtains, where state prices (relative to state probabilities) implicit in prices of traded securities will be inversely ranked to aggregate wealth across states. Our experiment broadly corroborates the price and choice predictions of the theory. However, price quality deteriorates when only a few managers attract most of the available wealth. Wealth concentration increases because funds flow toward managers who offer portfolios closer to replicating AD securities (as in the theory), but also because funds flow to managers who had better performance in the immediate past (an observation unrelated to the theory). This paper was accepted by Jerome Detemple, finance.
Decision-Making in Financial Markets
Publisher: Elsevier
Date: 2009
Common nonstationary components of asset prices
Publisher: Elsevier BV
Date: 06-1988
Neural Mechanisms Underlying Human Consensus Decision-Making
Publisher: Elsevier BV
Date: 04-2015
Best practices for building and curating databases for comparative analyses
Publisher: The Company of Biologists
Date: 08-03-2022
DOI: 10.1242/JEB.243295
Abstract: Comparative analyses have a long history of macro-ecological and -evolutionary approaches to understand structure, function, mechanism and constraint. As the pace of science accelerates, there is ever-increasing access to erse types of data and open access databases that are enabling and inspiring new research. Whether conducting a species-level trait-based analysis or a formal meta-analysis of study effect sizes, comparative approaches share a common reliance on reliable, carefully curated databases. Unlike many scientific endeavors, building a database is a process that many researchers undertake infrequently and in which we are not formally trained. This Commentary provides an introduction to building databases for comparative analyses and highlights challenges and solutions that the authors of this Commentary have faced in their own experiences. We focus on four major tips: (1) carefully strategizing the literature search (2) structuring databases for multiple use (3) establishing version control within (and beyond) your study and (4) the importance of making databases accessible. We highlight how one's approach to these tasks often depends on the goal of the study and the nature of the data. Finally, we assert that the curation of single-question databases has several disadvantages: it limits the possibility of using databases for multiple purposes and decreases efficiency due to independent researchers repeatedly sifting through large volumes of raw information. We argue that curating databases that are broader than one research question can provide a large return on investment, and that research fields could increase efficiency if community curation of databases was established.
Filtering Returns for Unspecified Biases in Priors when Testing Asset Pricing Theory
Publisher: Oxford University Press (OUP)
Date: 2004
Epilepsy and Ecstatic Experiences: The Role of the Insula
Publisher: MDPI AG
Date: 22-10-2021
Abstract: Ecstatic epilepsy is a rare form of focal epilepsy in which the aura (beginning of the seizures) consists of a blissful state of mental clarity/feeling of certainty. Such a state has also been described as a “religious” or mystical experience. While this form of epilepsy has long been recognized as a temporal lobe epilepsy, we have accumulated evidence converging toward the location of the symptomatogenic zone in the dorsal anterior insula during the 10 last years. The neurocognitive hypothesis for the genesis of a mental clarity is the suppression of the interoceptive prediction errors and of the unexpected surprise associated with any incoming internal or external signal, usually processed by the dorsal anterior insula. This mimics a perfect prediction of the world and induces a feeling of certainty. The ecstatic epilepsy is thus an amazing model for the role of anterior insula in uncertainty and surprise.
Promoting Intellectual Discovery: Patents Versus Markets
Publisher: American Association for the Advancement of Science (AAAS)
Date: 06-03-2009
Abstract: Because they provide exclusive property rights, patents are generally considered to be an effective way to promote intellectual discovery. Here, we propose a different compensation scheme, in which everyone holds shares in the components of potential discoveries and can trade those shares in an anonymous market. In it, incentives to invent are indirect, through changes in share prices. In a series of experiments, we used the knapsack problem (in which participants have to determine the most valuable subset of objects that can fit in a knapsack of fixed volume) as a typical representation of intellectual discovery problems. We found that our “markets system” performed better than the patent system.
Asset Pricing and Asymmetric Reasoning
Publisher: University of Chicago Press
Date: 02-2015
DOI: 10.1086/679283
Arbitrage Opportunities: Anatomy and Remediation
Publisher: Elsevier BV
Date: 2018
DOI: 10.2139/SSRN.3193856
Chapter 2 From Market Jaws to the Newton Method: The Geometry of How a Market Can Solve Systems of Equations
Publisher: Elsevier
Date: 2008
Local parametric analysis of derivatives pricing and hedging
Publisher: Elsevier BV
Date: 08-2003
Risk and Reward Preferences under Time Pressure*
Publisher: Oxford University Press (OUP)
Date: 27-06-2013
DOI: 10.1093/ROF/RFT013
Generic properties of a computational task predict human effort and performance
Publisher: Elsevier BV
Date: 09-2021
Experiments on Percolation of Information in Dark Markets
Publisher: Oxford University Press (OUP)
Date: 10-2017
DOI: 10.1111/ECOJ.12464
The Experimental Study of Asset Pricing Theory
Publisher: Now Publishers
Date: 2009
DOI: 10.1561/0500000022
Hedging Your Bets by Learning Reward Correlations in the Human Brain
Publisher: Elsevier BV
Date: 09-2011
“Lucas” in the Laboratory
Publisher: Wiley
Date: 10-11-2016
DOI: 10.1111/JOFI.12392
Abstract: We study the Lucas asset pricing model in a controlled setting. Participants trade two long‐lived securities in a continuous open‐book system. The experimental design emulates the stationary, infinite‐horizon setting of the model and incentivizes participants to smooth consumption across periods. Consistent with the model, prices align with consumption betas and comove with aggregate idends, particularly so when risk premia are higher. Trading significantly increases consumption smoothing compared to autarky. Nevertheless, as in field markets, prices are excessively volatile. The noise corrupts traditional generalized method of moment tests. Choices display substantial heterogeneity, with no subject representative for pricing.
An Exploration of Neo‐Austrian Theory Applied to Financial Markets
Publisher: Wiley
Date: 06-2001
Separate encoding of model-based and model-free valuations in the human brain
Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.NEUROIMAGE.2011.06.071
Abstract: Behavioral studies have long shown that humans solve problems in two ways, one intuitive and fast (System 1, model-free), and the other reflective and slow (System 2, model-based). The neurobiological basis of dual process problem solving remains unknown due to challenges of separating activation in concurrent systems. We present a novel neuroeconomic task that predicts distinct subjective valuation and updating signals corresponding to these two systems. We found two concurrent value signals in human prefrontal cortex: a System 1 model-free reinforcement signal and a System 2 model-based Bayesian signal. We also found a System 1 updating signal in striatal areas and a System 2 updating signal in lateral prefrontal cortex. Further, signals in prefrontal cortex preceded choices that are optimal according to either updating principle, while signals in anterior cingulate cortex and globus pallidus preceded deviations from optimal choice for reinforcement learning. These deviations tended to occur when uncertainty regarding optimal values was highest, suggesting that disagreement between dual systems is mediated by uncertainty rather than conflict, confirming recent theoretical proposals.
Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?
Publisher: Oxford University Press (OUP)
Date: 04-1999
DOI: 10.1093/RFS/12.2.405
Learning About Unstable, Publicly Unobservable Payoffs
Publisher: Oxford University Press (OUP)
Date: 03-10-2014
DOI: 10.1093/RFS/HHU069
The Neural Representation of Unexpected Uncertainty during Value-Based Decision Making
Publisher: Elsevier BV
Date: 07-2013
The Speed of Information Revelation and Eventual Price Quality in Markets with Insiders: Comparing Two Theories*
Publisher: Oxford University Press (OUP)
Date: 19-02-2013
DOI: 10.1093/ROF/RFS049
Asset trading volume in infinite-horizon economies with dynamically complete markets and heterogeneous agents: Comment
Publisher: Elsevier BV
Date: 06-2006
Toward a Mechanistic Understanding of Human Decision Making
Publisher: SAGE Publications
Date: 04-2008
DOI: 10.1111/J.1467-8721.2008.00560.X
Abstract: This article considers the contribution of functional neuroimaging toward understanding the computational underpinnings of human decision making. We outline the main processes likely underlying the capacity to make simple choices and describe their associated neural substrates. Relevant processes include the ability to encode a representation of the expected value or utility associated with each option in a decision problem, to learn such expectations through experience, and to modify action selection in order to choose those actions leading to the greatest reward. We provide several ex les of how functional neuroimaging data have helped to shape and inform theories of decision making over and above results available from traditional behavioral measures.
Human imagination in financial markets with insiders
Publisher: Elsevier BV
Date: 2007
Functionally and temporally dissociable neural processes underlying social decision-making on faces
Publisher: Elsevier BV
Date: 2007
Impaired recognition of the facial expressions in patients with Parkinson's disease
Publisher: Elsevier BV
Date: 2007
Neural Differentiation of Expected Reward and Risk in Human Subcortical Structures
Publisher: Elsevier BV
Date: 08-2006
DOI: 10.1016/J.NEURON.2006.06.024
Abstract: In decision-making under uncertainty, economic studies emphasize the importance of risk in addition to expected reward. Studies in neuroscience focus on expected reward and learning rather than risk. We combined functional imaging with a simple gambling task to vary expected reward and risk simultaneously and in an uncorrelated manner. Drawing on financial decision theory, we modeled expected reward as mathematical expectation of reward, and risk as reward variance. Activations in dopaminoceptive structures correlated with both mathematical parameters. These activations differentiated spatially and temporally. Temporally, the activation related to expected reward was immediate, while the activation related to risk was delayed. Analyses confirmed that our paradigm minimized confounds from learning, motivation, and salience. These results suggest that the primary task of the dopaminergic system is to convey signals of upcoming stochastic rewards, such as expected reward and risk, beyond its role in learning, motivation, and salience.
Investigating signal integration with canonical correlation analysis of fMRI brain activation data
Publisher: Elsevier BV
Date: 05-2008
DOI: 10.1016/J.NEUROIMAGE.2008.01.062
Abstract: How the brain integrates signals from specific areas has been a longstanding critical question for neurobiologists. Two recent observations suggest a new approach to fMRI data analysis of this question. First, in many instances, the brain analyzes inputs by decomposing the information along several salient dimensions. For ex le, earlier work demonstrated that the brain splits a monetary gamble in terms of expected reward (ER) and variance of the reward (risk) [Preuschoff, K., Bossaerts, P., Quartz, S., 2006. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381-390]. However, since ER and risk activate separate brain regions, the brain needs to integrate these activations to obtain an overall evaluation of the gamble. Second, recent evidence suggests that the correlation of the activity between neurons may serve a specific organizational purpose [Romo, R., Hernandez, A., Zainos, A., Salinas, E., 2003. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649-657 Salinas, E., Sejnowski, T.J., 2001. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539]. Specifically, it is hypothesized that correlations allow brain regions to integrate several signals in a way that minimizes noise. Under this hypothesis, we show here that canonical correlation analysis of fMRI data identifies how the signals from several regions are combined. A general linear model then verifies whether the identified combination indeed activates a projection area in the brain. We illustrate the proposed procedure on data recorded while human subjects played a simple card game. We show that the brain adds the signals of ER and risk to form a measure that activates the medial prefrontal cortex, consistent with the role of this brain structure in the evaluation of monetary gambles.
Related Organisations
California Institute Of Technology
Location: United States of America
University Of Cambridge
Location: United Kingdom of Great Britain and Northern Ireland
Related Funding Activities
Discovery Projects - Grant ID: DP180102284
Start Date: 05-2018
End Date: 06-2022
Amount: $364,921.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP180100424
Start Date: 12-2019
End Date: 12-2022
Amount: $207,497.00
Funder: Australian Research Council
View Funded Activity