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xxx12 Behavioural Analytics



Upon completion of the course, the student can: 

  • differentiate between the economic agent/decision maker in standard economics and the economic agent/decision maker within behavioural economics, 
  • explain how selected behavioural models of decision-making works and differs from the expected utility theory in standard economics, 
  • give examples of behavioural research evidence from academia and within business, economics and finance and 
  • summarize and critically assess the main findings of empirical research on behavioural evidence within business, economics and finance. 


Upon completion of the course, the student can: 

  • plan a study using an experimental design, 
  • design and conduct an experimental study (e.g., a survey experiment) aiming to shed light on a causal question, 
  • analyse quantitative data from experimental designs and/or other data sources reflecting people’s actual preferences, decision processes and choices and 
  • demonstrate professional reporting and writing skills by preparing a short academic paper of high quality on the topics covered in the course. 

General competence 

Upon completion of the course, the student can: 

  • complete a research project built on the analysis of behavioural evidence, 
  • debate findings from research on behavioural evidence with peers and 
  • critically assess the conclusions of prior behavioural research.


The following topics are covered. 

  • Introduction: Studying behaviour as it is—and not as it should be—according to standard (i.e., normative) economic theory 
  • Foundations of behavioural economic analysis: A non-technical review of key concepts (e.g., bounded rationality) and empirical evidence from business, economics and finance 
  • The logic of experimental designs 
  • Supervised learning techniques (classification: logistic regression and related techniques) 
  • Unsupervised learning techniques (factor/cluster analysis and related techniques) 
  • Text mining and sentiment analysis 
  • Academic research project which involves data collection, analyses and reporting

Arbeids- og undervisningsformer

The use of real life data from a range of decision situations (e.g., experiments, transaction data on purchases, web browsing data, online gaming data on user trends and preferences) and the use of experimental research designs play a key role in the course. The following teaching methods are used. 

  • Lectures 
  • Tutorial videos 
  • Research-based teaching 
  • Case studies 
  • Project work in teams

Obligatoriske krav som må være godkjent før eksamen kan avlegges

  • Students must pass three out of four mandatory course requirements to be allowed to take the exam. 
  • 50% attendance in classes is required. 


  • Group project (two students per group): The students shall plan, prepare and execute an experimental study (e.g., a survey experiment). The data collected shall be analysed and reported in a final written academic report. 

Tillatte hjelpemidler til eksamen

  • All resources.