The teaching consists of 40 lessons, in lessons of either 4 or 2 hours. 

Financial decisions are among the most important and difficult decisions individuals and organizations face. Yet they are systematically affected by behavioral biases, especially in periods of uncertainty and market stress.

This course develops a structured approach to understanding and improving decision-making under uncertainty.

The course examines how individuals make decisions under risk and uncertainty, using financial contexts as a rich and realistic setting. Financial decisions, such as investing, saving, and reacting to market news, provide clear and measurable examples of how beliefs, emotions, and social influences shape behavior.

The course combines rigorous academic insights from behavioral economics, psychology, and finance with applied, real-world decision-making scenarios. Each case studied in the course is grounded in well-established theoretical frameworks and supported by empirical evidence from the academic literature.

While many examples are inspired by investor and advisory contexts, financial decision-making is used as a high-resolution lens to study general principles of human behavior. The insights are therefore broadly applicable across business, policy, and everyday decision-making. The course is particularly relevant for roles involving decision-making under uncertainty, including finance, consulting, management, and advisory contexts.

A central feature of the course is a set of realistic decision-making cases, which are used throughout the course to connect theory and practice. These cases reflect situations where decisions must be made under uncertainty, often with strong beliefs, conflicting objectives, and emotional reactions.

Examples of cases explored in the course include:

• A high-net-worth individual with a strong belief in a single stock (e.g., Novo Nordisk), illustrating overconfidence, confirmation bias, and selective attention.

• Investors who react strongly to short-term losses in liquid portfolios while ignoring larger long-term fluctuations, illustrating mental accounting and limited attention.

• Investors shifting to assets such as gold in response to geopolitical uncertainty and media narratives.

• ESG investing decisions where sustainability preferences, social norms, and narratives influence portfolio choices and perceived trade-offs between values and returns.

• Household portfolio decisions where differences in income, risk preferences, and confidence affect outcomes.

Each case is explicitly linked to core theoretical models and empirical findings in the literature, and is used as a prototypical example of high-stakes decision-making under uncertainty.

By the end of the course, students will be able to apply a structured toolkit for analyzing and improving decisions under uncertainty. This includes identifying behavioral biases in practice, evaluating their impact on outcomes, and designing concrete strategies to improve decisions.

The course is structured around a consistent analytical approach to decision-making:

1. Observation – What is happening in the decision situation?

2. Diagnosis – Which behavioral mechanisms explain the observed behavior?

3. Evaluation – How do these mechanisms affect decisions and outcomes?

4. Implementation – What can be done to improve decisions, and how can it be effectively put into practice?

This framework is used throughout the course to analyze cases and develop practical decision-making skills.

Conceptual scope

The course integrates a coherent set of theoretical frameworks from:

• Behavioral economics (heuristics and biases, prospect theory, intertemporal choice)

• Psychology and cognitive science (attention, belief formation, judgment under uncertainty)

• Finance (investor behavior, asset pricing anomalies, market dynamics)

• Selected contributions from neuroscience (emotions and decision-making under risk)

These frameworks are used systematically to interpret behavior, structure decision problems, and design improved decision strategies.

These frameworks are introduced with a focus on intuition and practical relevance, rather than technical complexity. The emphasis is on understanding how the mechanisms work in real-world situations and how they can be applied to improve decisions.

The course emphasizes intuition, interpretation, and application, while maintaining a strong foundation in theory and rigorous empirical evidence.

Students participating in the course are expected to have a basic understanding of statistics and be able to make usual financial calculations such as present value calculations and expected values. Similarly, a general knowledge of financial markets, financial institutions and financial instruments such as options is assumed.