Most decisions must be made without knowing in advance what their consequences will be. For instance, the decision to go to court or settle a civil suit must be made without knowing inadvance whether the court will decide in one’s favor; the decision whether or not to purchase insurance must be made without knowing in advance whether there will be a need for a claim. In decisions under risk, probabilities of consequences are known precisely by the decision maker (as is the case with simple chance gambles); in decisions under uncertainty they are not (as is this case with most other decisions). Our lab is investigating the impact of uncertainty in decision making. Specifically we have a number of projects aimed at understanding the impact of: (1) sampled experience, (2) probabilistic beliefs; (3) risk and ambiguity preferences in governing such choices. Current projects are focused on: (a) modeling relative and absolute likelihood judgment, (b) understanding the differences between description-based decision making and experience-based decision making, and (c) characterizing the intuitive distinction between confidence that a statement is true or an event will occur (epistemic uncertainty) and belief in the propensity with which a particular event will occur (aleatory uncertainty).

[papers on decision under uncertainty & risk]



pastedgraphic-3In our research we find that people intuitively distinguish two dimensions of subjective uncertainty. In some cases people attribute uncertainty to deficiencies in their knowledge, information, and/or mental model of relevant events (knowable or “epistemic” uncertainty); in other cases people attribute uncertainty to causal systems in the world whose behavior is largely stochastic (random or “aleatory” uncertainty). We find that epistemic (knowable) uncertainty is marked in natural language by statements such as “I am 80% sure that…” or “I am reasonably confident that…” whereas aleatory (random) uncertainty is marked by statements such as “I think there is an 80% chance that…” or “I believe there is a high probability that…”. We also find that people reliably distinguish between epistemic and aleatory uncertainty in their rating of events, and forecasters are assigned more credit/blame for correct/incorrect predictions when events are seen as more epistemic (knowable) whereas they are seen as more lucky/unlucky when events are seen as more aleatory (random). In our work we find several implications for judgment and decision making. For instance people tend to make more extreme probability judgments (and therefore exhibit greater overconfidence) when assessing events that they see as more epistemic (knowable) or less aleatory (random). Moreover, investing behaviors (e.g., time horizon, diversification, advice-seeking) are associated with individual differences in perceptions of market uncertainty.

[papers on variants of uncertainty]



When people distribute beliefs, resources, or choices over events, beneficiaries, or consumption options, they tend to be biased toward even allocation over the groups into which possibilities are organized. Thus, judgments and choices vary systematically with the way in which possibilities happen to be grouped, a phenomenon known as “partition dependence.” We have found experimental evidence of partition dependence in a variety of substantive domains. In judgment under uncertainty, judged probabilities tend to be biased toward 1/n for each of n events into which the state space is partitioned. In resource allocation decisions, people tend to be biased toward even distribution over the groups into which the set of investments or beneficiaries are partitioned. In consumer choice, people tend to seek variety over the salient categories into which the menu of consumption options is partitioned. In continuing research we are exploring manifestations of partition dependence in capital budgeting decisions and prediction markets. We are also testing the “ignorance prior” model of judgment under uncertainty (a refinement of support theory) and measuring its parameters.

[papers on partition dependence]



Choice Architecture & Behavioral PolicyInsights from behavioral decision theory can be applied to helping “nudge” people to make better decisions without undermining their freedom to choose. This entails setting up “choice architecture” that is conducive to the desired behavior. We have a special interest in nudging health care providers to make better decisions on behalf of their patients. For instance, in recent work funded by the National Institutes of Health (NIH) we have shown how behavioral insights can provide methods for curtailing doctors’ tendencies to inappropriately prescribe antibiotics for upper respiratory infections.

[papers on choice architecture]





dice_chipsMuch of our work on risk and uncertainty has been relevant to financial decision making. In addition we have recently begun to explore saving, debt, and time preferences. Our hope is that this work can be used to help consumers make better financial decisions and help professionals in the financial services industry provide better advice to their clients.

[papers on financial decision making]







Busy BeesOur research is highly interdisciplinary, focusing on judgment and decision processes that are of relevance to a variety of fields including management. Several of our papers address topics that are of special interest to managers, including strategic management, negotiation and conflict resolution, and organizational behavior.

[papers on strategy, negotiation, & organizational behavior]






We have recently begun studies of decision neuroscience. In collaboration with the Poldrack Lab we have been using fMRI to explore how the brain responds to decisions under risk, both in static and dynamic contexts. In addition we have been exploring the role of affect in risky choice, and risk-taking in clinical populations.

[papers on decision neuroscience]