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    • Decision-Making under Uncertainty: An Introduction to Neuroeconomics and Neurofinance

      Semester & Location:       Fall Semester · Unimail Room M1160 · Tuesdays, 10:15–12:00

      Course Overview

      This foundational course explores decision-making under uncertainty through the combined lenses of economics/finance, psychology, and neuroscience. Rather than treating these as separate viewpoints, the course builds a shared vocabulary across disciplines — starting with how each field defines value, utility, reward, and wealth — and then examines the core methods (behavioral, neural, and computational) used to study decisions under uncertainty.
      Building on this foundation, the course turns to uncertainty itself: how risk, ambiguity, and surprise are formally distinguished, and how emotion, mood, context, and social interaction systematically shape decisions even when the information available stays the same.
      The course closes with a set of real-world applications — drawn from current research on topics such as trading, sustainable decision-making, and strategic foresight — each designed to show why a single disciplinary perspective is insufficient to fully understand complex decisions under uncertainty.
      A recurring theme throughout is individual variability: population-level models can describe group behavior well while telling us little about any one person, a distinction the course returns to in nearly every topic.
      Some material is covered in-class; other perspectives and deeper technical content are provided through self-guided online materials each week.
      This course lays the theoretical and methodological groundwork for the follow-up course offered in the spring, which focuses on recent research and applied case studies in neuroeconomics and neurofinance.

      Target Audience

      • Master’s and Ph.D. students in economics, finance, neuroscience, psychology, and computer science

      • Open to all students with an interest in decision-making and interdisciplinary approaches

      Format

      Workload: this is a 6 ECTS course, corresponding to approx. 150 to 180 hours in total. The workload is split into lectures, self-study, assignments, and reading exercises. Organize your weekly workload according to the following typical split: 

      • Weekly in-person lectures (90 minutes)

      • Weekly self-paced learning (2-3h) including short videos, podcasts, and exercises

      • Homework assignments (4 in total on weeks 3,5,7 & 9)

      Grading:

      Attendance and class participation (10%) + Assignments pass or fail (30%) + Final exam (60%)

      Learning Outcomes

      By the end of the course, students will be able to:

      1. Explain and compare how economics/finance, psychology, and neuroscience each formalize core concepts in decision-making under uncertainty (e.g., value, utility, reward).
      2. Describe the behavioral, neural, and computational methods used to study decisions under uncertainty, and identify which method is appropriate for a given question.
      3. Formally distinguish risk, ambiguity, and surprise, and explain how emotion, mood, context, and social factors modulate decisions independently of the information available.
      4. Analyze real-world cases of decision-making under uncertainty by integrating multiple disciplinary perspectives, recognizing where single-discipline accounts fall short.
      5. Recognize the limits of population-level models for understanding individual decision-making.
      6. Demonstrate the foundational knowledge needed for the spring follow-up course, which builds on these concepts to examine current research and applications in neuroeconomics and neurofinance.

      Instructors:

      • Kerstin Preuschoff : kerstin.preuschoff@unige.ch
      • Marco Lehmann : marco.lehmann@unige.ch

      Teaching Assistant:

      • Jean-Paul Nohra : jean-paul.nohra@unige.ch

  • Week 1 — Introduction & Course Roadmap (15.09.2026))

    • Description

      Decision-making under uncertainty is inherently interdisciplinary — no single lens (economics, psychology, or neuroscience) is sufficient on its own, and this course is structured to prove that claim repeatedly rather than just assert it once.



      Learning Objectives:

      • Explain why decision-making under uncertainty requires economic, psychological, and neuroscientific perspectives together, not separately.

      • Preview each week's topic through a running example (an investment decision) that gets reanalyzed at each stage of the semester.

      • Distinguish, at an intuitive/informal level, risk, ambiguity, and uncertainty as everyday terms.

      • Introduce the population-vs-individual theme explicitly (the "height analogy") as a thread that resurfaces most weeks.

      • Course logistics: structure, assessment, self-study format, expectations.

    • In-Class Activity (90 mins)

    • Week 1 - Slides Datei
      Hochgeladen 15.09.2026 13:54

    • Self-Study (2-4 hours per week)

    • Mandatory Reading Datei

      Meder, B., Le Lec, F. and Osman, M., 2013. Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises?. Trends in Cognitive Sciences, 17(6), pp.257-260.

      Comments and Guiding Questions:

      1. Reading Guide: Read the abstract twice. Then carefully read Box 1 and Figure 1. Finally, read the entire paper and answer the guiding questions below..
      2. According to Knight's distinction as the paper presents it, what exactly separates "risk" from "uncertainty"?
      3. Using the paper's three sources of uncertainty (Figure 1A: Actor, Others, World), identify at least one source in each category for the investment decision from lecture (the friend's tip company, slide 4).
      4. The Lecture slides introduce 5 levels of uncertainty (slide 29) and Types of uncertainty (slide 30). Where do the paper (Figure 1) and the lecture slides agree? Where do they differ?
      5. The paper argues that lottery-style lab tasks limit what we can learn about real decisions like the eurozone bond example. Why, according to the authors?
      6. The paper does not present primary empirical results. Instead it diagnoses a conceptual problem and proposes a research direction. What is that problem? How could future research address it?
      7. We will post possible answers during next week for you to compare your work.

      NB: no submission required. This is to guide your personal work, and might be useful to take your own work.

      Hochgeladen 15.09.2026 14:54

    • Optional: Feed Your Curiosity

    • Optional Reading Datei

      Spitz, Roger, and Olivier Desbiey. "The future of risk and insurability in the era of systemic disruption, unpredictability and artificial intelligence." Journal of Operational Risk (2025).

      Hochgeladen 15.09.2026 13:45

  • Week 2 — Value, Utility, Gain, Reward, Wealth (Part 1) (22.09.2026)

    • Description

      The same underlying idea — "this matters to me" — gets formalized differently across discipline
      Building an explicit taxonomy makes those differences visible instead of assumed, and gives students a shar
      vocabulary to carry through the rest of the semester



      Learning Objectives:

      1. Construct a taxonomy distinguishing value, wealth, utility, expected value, reward, and expected reward across
      everyday, economic, psychological, and neuroscientific usage

      2. Narrate the historical economic progression from value to probability to utility (Bernoulli → von Neumann–
      Morgenstern), identifying what problem each step solved.

      3. Identify structural parallels between economic utility and neuroscientific reward as candidate formalizations 
      "value," without yet claiming they're equivalent.

      4. Recognize that probability/uncertainty is being introduced only instrumentally here — enough to follow the utility
      story — and will be treated as a topic in its own right later (weeks 7–8)

      5. Narrate the psychological progression (more details in self study)

    • In-Class Activity (90 mins)

    • Week 2 - Slides Datei
      Hochgeladen 22.09.2026 15:00
    • Values - Padlet Link/URL

      Link: https://padlet.com/unige/what-do-you-value-s023t8a2fgf5jyn13zmg?pw=KfVfUKOCSYgiCr2zbXoXTA

      Password: DMUU1

    • Self-Study (2-4 hours per week)

    • Self Study Week 02 Datei
      Self-Check Questions
      Companion to "Psychology's History of Value, Reward, and Motivation"

      These are for your own use — not an assignment, not submitted, not graded. They're designed to test the connections between the two history documents (economics and psychology), not to test whether you can recall any single card. If you can answer these, you've read both documents the way they're meant to be read.

      1. Economics removed mental states from its core theory through Hicks & Allen and Samuelson, in 1934–1938. Psychology's parallel move — Thorndike and Skinner's behaviorism — spans almost the same years, 1898–1938. What is genuinely the same about these two removals, and what is different about why each field made the move?

      2. Atkinson's expectancy-value formula (1957) and von Neumann and Morgenstern's expected utility (1944) share the same mathematical structure — a probability-like term multiplied by a value term — arrived at independently, thirteen years apart. Name one plausible reason this same structure would show up twice, in two fields that weren't reading each other's work.

      3. Look across the seven psychology entries as a group. Does "mental states in or out" move in one direction over time, or does it go back and forth? What does your answer imply about whether Samuelson's removal of mental states from economics (this week's §4) was a permanent, settled move, or one instance of a pattern that keeps recurring?

      4. Hull and Maslow were both publishing in 1943, working on essentially the same broad topic — what drives behavior — and reached very different positions on how to treat internal, unobservable states scientifically. In two or three sentences, contrast their positions.

      5. No single published paper covers both of these histories together — you needed two separate documents to see the parallel at all. What does that suggest about how academic fields organize themselves, even when they are, in a real sense, studying the same underlying question from two directions?

      PS: the references at the bottom of the document are not to be read. Only reading required is the document and use the attached questions to guide and help your studying

      Hochgeladen 23.09.2026 16:34

    • Optional: Feed Your Curiosity

  • Week 3 — Methods I: The Methodological Landscape (with a Behavioral Focus) (29.09.2026)

    Aktuelle Woche

  • Week 4 — Value, Utility, Gain, Reward, Wealth (Part 2): Physiological & Neural Basis (06.10.2026)

  • Week 5 — Methods II: Learning & Updating I (Reinforcement Learning) (13.10.2026)

    • Assignment 2
      Due date: October 19th at noon (19.10.2026 at 12 PM)

  • Week 6 — Methods III: Learning & Updating II (Bayesian Learning) (20.10.2026)

  • Week 7 — Formal Foundations: Risk, Ambiguity, Uncertainty (27.10.2026)

    • Assignment 3
      Due date: November 2nd at noon (02.11.2026 at 12 PM)

  • Reading Week - no class (03.11.2026)

    • No In-Class Activity

    • Self-Study (2-4 hours per week)

    • Optional: Feed Your Curiosity

  • Week 8 — Surprise, Unexpected Uncertainty & the Brain as Inference Machine (10.11.2026)

  • Week 9 — Modulators of Decision-Making I: Emotion, Mood & Context (17.11.2026)

    • Assignment 4
      Due date: November 23rd at noon (23.11.2026 at 12 PM)

  • Week 10 — Modulators of Decision-Making II: Social Interaction & Strategic Uncertainty (24.11.2026)

  • Weeks 11 — Applications (01.12.2026)

  • Weeks 12 — Applications (08.12.2026)

  • Weeks 13 — Applications (15.12.2026)