About

Manel Baucells is the Lynch Family Associate Professor of Business Administration at UVA Darden School of Business. His academic area is Data Analytics and Decision Sciences, with expertise in decision analysis, consumer behavior, game theory, and business models. Baucells completed his education with an M.S. in Mechanical Engineering from Universitat Politècnica de Catalunya, an MBA from IESE Business School, and a Ph.D. from the Anderson School at UCLA. His doctoral research focused on game theory with applications to management, supervised by Steve Lippman and Lloyd Shapley, a Nobel laureate in economics. Prior to joining Darden in 2015, Baucells held positions at the Rand Corporation, University Pompeu Fabra in Barcelona, and IESE Business School. His research emphasizes incorporating psychological realism into consumer behavior models by considering factors such as reference point formation, mental accounting, psychological distance, anticipation, fatigue, and satiation. These factors influence risk and time preferences, consumer choices, and managerial decisions. Baucells has a broad international experience, lecturing in North America, Europe, China, Russia, Latin America, and Africa, and has held visiting research positions at prestigious institutions including Duke University, UCLA, London Business School, MIT, and Erasmus University. He has received multiple awards for excellence in research and teaching, and he co-authored the book 'Engineering Happiness,' which applies behavioral economics principles to improve life outcomes.

Research topics

• Computer Science
• Artificial Intelligence
• Programming language
• Microeconomics
• Economics
• Mathematical economics
• Econometrics
• Psychology
• Mathematics

Selected publications

• The Silver Crest Mine

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• BEACON: Bayesian Optimal Stopping for Efficient LLM Sampling

  ArXiv.org · 2025-10-09

  preprintOpen access

  Sampling multiple responses is a common way to improve LLM output quality, but it comes at the cost of additional computation. The key challenge is deciding when to stop generating new samples to balance accuracy gains against efficiency. To address this, we introduce BEACON (Bayesian Efficient Adaptive Criterion for Optimal N-stopping), a principled adaptive sampling framework grounded in Sequential Search with Bayesian Learning. BEACON sequentially generates responses from the policy LLM, updates posterior belief over reward distributions in real time without further training, and determines when to stop by weighing expected gains against computational cost. Sampling terminates once the marginal utility of further exploration no longer justifies the expense. We establish both theoretical optimality guarantees and practical tractability, and show empirically that BEACON reduces average sampling by up to 80% while maintaining response quality. We further demonstrate BEACON's utility for cost-efficient preference data generation and outline practical extensions, offering actionable insights for future researchers.

  PublisherOA PDFDOI

• Magnitude Effects and Decreasing Impatience in Temporal Discounting: Models, Evidence, and Applications

  Journal of Economic Behavior & Organization · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Search without Recall and Gaussian Learning: Structural Properties and Optimal Policies

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• On the Properties of the Metalog Distribution

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• On the Properties of the Metalog Distribution

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Direction of impact for explainable risk assessment modeling

  Risk Analysis · 2025-02-25 · 2 citations

  articleOpen access

  Several graphical indicators have been recently introduced to help analysts visualize the marginal effects of inputs in complex models. The insights derived from such tools may help decision-makers and risk analysts in designing interventions. However, we know little about the adequacy and consistency of different indicators. This work investigates popular marginal effect indicators to understand whether they yield indications consistent with the properties of the quantitative model under inspection. Specifically, we examine the notions of monotonicity, Lipschitz, and concavity consistency. Surprisingly, only PD functions satisfy all these notions of consistency. However, when selecting the indicators, in addition to consistency, analysts need to consider the risk of model extrapolation. For situations where such risk is under control, we utilize individual conditional expectations together with PD plots. Two applications, on a NASA space risk assessment model and a susceptible exposed infected recovered (SEIR) model for the COVID-19 pandemic illustrate the insights obtained from these indicators.

  PublisherDOI

• Managerial Mental Accounting and Downstream Project Decisions

  Management Science · 2024-03-04 · 7 citations

  article1st authorCorresponding

  Project leaders are responsible for planning, controlling, and revising projects. As a project unfolds, the leader evaluates the project’s progress by comparing ongoing costs and scope to a baseline plan and considers potential revisions. We offer a general model of managerial mental accounting, which includes loss aversion, reference point updating, and narrow framing, and examine how it impacts downstream decisions. Our model predicts insufficient adjustments of project scope and cost at revision, resulting in reduced financial profit. We show that the choice of measure to quantify the project progress—planned, actual, or earned—affects the updating of reference points, and hence the downstream decisions. Thus, progress measures could be wisely employed to mitigate insufficient adjustments. It turns out that measuring progress via planned scope is often advantageous, whereas utilizing earned value for cost is never advisable. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02929 .

  PublisherDOI

• The Discount Rate for Investment Analysis Applying Expected Utility

  Decision Analysis · 2024-02-20 · 5 citations

  article1st authorCorresponding

  In decision analysis, expected utility of discounted cash flows is the traditional approach to incorporate risk aversion into the evaluation of a project. The choice of discount rate as well as the convergence with the beta-adjusted approach from finance have always been in question. To address this gap, we adopt a risk-sharing setup in which investors have both treasuries and the stock market as alternatives to the project. For a full utility analysis of all the investor’s capital, we provide a unique discount rate that allows setting the horizon at the termination of the project. For a traditional analyst who conducts expected utility of discounted cash flows and ignores the capital not allocated to the project, we recommend an adjusted discount rate that compensates for double-counting the systematic risk.

  PublisherDOI

• Search in the Dark: The Case with Recall and Gaussian Learning

  Operations Research · 2024-08-16

  article1st authorCorresponding

  In “Search in the Dark: The Case with Recall and Gaussian Learning,” Manel Baucells and Saša Zorc address the classic sequential search problem where decision makers sample from a distribution to maximize rewards minus sampling costs. They focus on search with recall, where the reward is the highest sampled value. They solve the long-standing problem of determining optimal stopping rules when the sampling distribution is normal but both its mean and variance are unknown, and hence, they are progressively learned via sampling. The solution reveals that traditional methods, which rely on single reservation prices, are inadequate. The proposed approach offers a practical and efficient way to determine the optimal stopping rule. Relevant applications include job search, the sale of an asset, or technology adoption.

  PublisherDOI

Frequent coauthors

• Rakesh K. Sarin

  Anderson University - South Carolina

  33 shared

• Franz H. Heukamp

  9 shared

• Gerry Yemen

  7 shared

• Lin Zhao

  University of Chinese Academy of Sciences

  7 shared

• Samuel E. Bodily

  University of Virginia

  6 shared

• Woonam Hwang

  Korea University Medical Center

  6 shared

• Michał Lewandowski

  SGH Warsaw School of Economics

  6 shared

• Cristina Rata

  4 shared

Education

• Ph.D.

  UCLA Anderson School of Management

  1999

Awards & honors

• Excellence in Course Development Award (at IESE)
• Excellence in Research Award (at IESE)
• Dean's Award for Excellence (at Darden)
• Multiyear Publication Award, 2015-2018 (at Darden)
• Best Publication Award by the Decision Analysis Society (201…