Welcome

About Me

I am an Assistant Professor in the Department of Economics at the University of Vienna.

Prior to that, I was a Postdoctoral Research Fellow in the Department of Economics at the University of Toronto where I was also affiliated with Behaviorally Informed Organizations (BI.Org) - Behavioral Economics in Action (BEAR) based at the Rotman School of Management. I was also the manager of the Toronto Experimental Economics Lab (TEEL). If you are interested in reading more about the work we did at TEEL, U of T News published a great article: ``Decisions, decisions: TEEL at U of T studies how and why we make choices."

My main research interests are in the field of microeconomic theory, behavioral and experimental economics.

My research interests are individual and strategic decision making, which I study using theoretical and experimental tools. I am especially interested in strategic learning in its various settings and in what constitutes rational behavior and how rationality or bounded rationality can be revealed in various environments.

In the fall term, I teach Microeconomics and in the spring term Decision Theory & Game Theory.

Publications
Working Papers
Work in Progress
Courses Taught
Research

Publications

Bandits in the Lab
with Nicolas Klein (U de M).

Quantitative Economics (July 2021), Volume 12, Issue 3, pp. 1021–1051.

John McMillan Prize for the Best Paper in Economics by a PhD Student

We experimentally implement a dynamic public-good problem, where the public good in question is the dynamically evolving information about agents' common state of the world. Subjects' behavior is consistent with free-riding because of strategic concerns. We also find that subjects adopt more complex behaviors than predicted by the welfare-optimal equilibrium, such as non-cut-off behavior, lonely pioneers and frequent switches of action.

Go to paper | Online appendix | Cool poster | Heatmaps & videos

Essays in Theoretical and Experimental Economics

Journal & Proceedings of the Royal Society of New South Wales (2018), Volume 151, Issue 2, pp. 232-234.

Business School Higher Degree Research Student Thesis Award for the Best Doctoral Dissertation

Thesis abstract of my doctoral dissertation (commissioned submission).

Go to paper

Research

Working Papers

Magic Mirror on the Wall, Who Is the Smartest One of All?
with Yoram Halevy (U of T) & Terri Kneeland (UCL). September 2022

New version coming soon!

In the canonical model of bounded rationality each player best-responds to their belief that other players reason to some finite level. We propose a novel behavior that reflects the player's belief that while other players may be rational, the player cannot model and hence predict the behavior of others. This encompasses a situation where a player believes that their opponent can reason to a higher level than they do. We propose an identification strategy for such behavior, and evaluate it experimentally.

Go to paper | The diagnostic + control games

The Streetlight Effect in Data-Driven Exploration
with Gustavo Manso (Berkeley), Abhishek Nagaraj (Berkeley) & Matteo Tranchero (PhD student @ Berkeley). August 2022

New paper!

Nominated for the Best Conference Paper Prize of the SMS Milan Special Conference

We consider settings such as innovation-oriented R&D where agents must explore across different projects with varying but uncertain payoffs. How does providing partial data on project payoffs affect individual performance and social welfare? While data can typically reduce uncertainty and improve welfare, we present a simple theoretical framework where data provision can decrease group and individual payoffs. In particular, we predict that when data shines a light on sufficiently attractive (but not optimal) projects, it can crowd-out exploration activity, lowering individual and group payoffs as compared to the case where no data is provided. We test our theory in an online lab experiment where we show that data provision on the true value of one project can hurt individual payoffs by 12% and reduce the group's likelihood of discovering the optimal outcome by 48%. Our results provide a theoretical and empirical foundation outlining the conditions under which the streetlight effect emerges, where data leads agents to look under the lamppost rather than engage in individually and socially beneficial exploration.

Go to paper | Online appendix | Interface & videos | Comic

Coordination in the Network Minimum Game
with Hongyi Li (UNSW). August 2022

New version!

Motivated by the problem of organizational design, we study coordination in the network minimum game: a version of the minimum-effort game where players are connected by a directed network. We show experimentally that acyclic networks such as hierarchies are most conducive to successful coordination. Introducing a single link to complete a network cycle may drastically inhibit coordination. Further, acyclic networks enable resilient coordination: initial coordination failure is often overcome (exacerbated) after repeated play in acyclic (cyclic) networks.

Go to paper | Online appendix | Network structures

Asset Ownership and the Hold-Up Problem with Asymmetric Information
with Richard Holden (UNSW). September 2021

We study a contract environment with an ex-ante investment stage and where ex-post bargaining takes place under one-sided asymmetric information. We offer a model where only the presence of an outside option allows for approximately ex-ante efficiency. Without an outside option, any static or sequential mechanism performs worse, which we view as a rationale for the role of ownership allocation in contracting environments with asymmetric information. We take these theoretical predictions to a laboratory setting and find that outside options as implemented through asset ownership are valuable, not only because of somewhat more efficient ex-ante investment but because they reduce ex-post frictions.

Go to paper | Online appendix

Research

Work in Progress

Anticipated Regret
with David Dillenberger (Penn), Yoram Halevy (U of T) & Gideon Nave (Penn).

Coming soon

A well-known phenomenon in the decision science literature is that anticipated regret affects choices and valuations. We analyze Kahneman & Tversky's (1979) famous decision problem of the certainty effect – a special case of the common ratio effect á la Allais (1953) as well as extensively documented probability insensitivity in mid-ranges. We propose that these phenomena are, in fact, manifestations of anticipated regret; offer a behavioral definition of anticipated regret without committing to a specific functional representation; and document evidence of anticipated regret in a controlled lab setting. We find that more than half of our participants exhibit strict Certainty Effect, and about two-fifths of them exhibit aversion to anticipated regret.

Present Bias under Uncertainty
with Nazlı Billur Görgülü (PhD student @ U of T) & Yoram Halevy (U of T).

Coming soon

We investigate time preferences under uncertainty. Our experiment allows the direct comparison of time preferences for certain, risky, or ambiguous future prospects using choice lists. We present participants with a set of choice problems with two options each: an earlier smaller reward to be paid at period 𝑡, and a later larger reward to be paid at period 𝑡 + 𝑘. The results of the experiment suggest a significant impact of risk and ambiguity on time preferences. We document a lower incidence of decreasing impatience for risky prospects compared to certain or ambiguous prospects.

Teaching

Courses

This is an introductory course in microeconomics. In the first part of the course, students are introduced to the benchmark of perfectly competitive markets. In the second part, we explore ways in which actual markets differ from this benchmark such as market power, externalities, public goods, and asymmetric information problems. We conclude by discussing the scope and role of government intervention in markets. This course serves three main purposes: (i) to introduce students to basic microeconomic principles; (ii) to help students understand the business world through the lens of economic models; (iii) to develop students' skills in critical thinking and comprehension. In this course, we cover the following topics:

  • Demand
  • Supply
  • Competitive market equilibrium and equilibrium welfare
  • Market power and oligopolies
  • Market power and monopolies
  • Externalities
  • Public goods and redistribution
  • Information frictions between consumers and firms
  • Information frictions within organisations

The current syllabus can be found here.

Game theory is a set of tools for studying situations in which decision-makers (like consumers, firms, politicians, and governments) interact. This course provides an introduction to game theory, with a strong emphasis on applications in economics. The objective of the course is to give students an understanding of the core concepts of game theory and how to use them to understand economic, social, and political phenomena. Through making decisions in classroom experiments students experience many different strategic situations first hand. In this course, we cover the following topics:

  • Strategic games and Nash equilibrium
  • Cournot’s and Bertrand’s models of duopoly
  • Hotelling’s model of electoral competition and the citizen-candidate model
  • Mixed strategy Nash equilibrium, with applications
  • Dominated strategies and iterated elimination of dominated strategies and common knowledge of rationality
  • Strategic games with imperfect information and auctions
  • Extensive games and subgame perfect equilibrium
  • Ultimatum game and holdup game
  • Repeated games and collusion in repeated duopoly
  • Extensive games with imperfect information and signaling games

This course provides an introduction to basic analytical skills. The course provides a solid basis from which data analysis techniques and tools can be applied to solve business problems. Therefore, there is an emphasis on problem solving and business analytics by both manual and computer methods. The first six lectures focus on the use of quantitative methods and techniques. The second six lectures focus on the use of qualitative research methods and techniques

Mathematics is an important part of theoretical and applied analysis in economics and business. This course equips students with a working knowledge of the most common techniques, providing the basis for their further studies. Topics include the mathematics of finance, matrix algebra, linear programming, as well as calculus and (unconstrained and constrained) optimization. Special emphasis is put on the illustration of the covered concepts and techniques with applications to typical problems in business and economics.

This unit examines the economic performance and policies of the European Union and of some of its leading members. It analyzes key political and economic issues affecting the successful integration of the European Union, and examines their effect on member countries. Overall, it intends to be a comprehensive and up-to-date overview of the economics and politics of the world's largest market.

This unit examines international trade both in theory and practice. It first reviews the theories related to inter- and intra-industry trade determination and the empirical evidence supporting them. It then examines trade policies, covering a wide range of topics, illustrated by up-to-date case studies. The unit has a policy approach and aims at also providing students with a good understanding of major trade issues around the world and the effects of globalisation on trade. It focuses on current applications of theoretical principles.

Additional Material

Supplement to
Magic Mirror on the Wall, Who Is the Smartest One of All?

Here you can find examples of the interfaces subjects saw during the experiment. Participants faced four games in random order. To improve participants' experience and to assist in selecting an action, we implemented a highlighting tool that used two colors: yellow and light green. When a participant moved their mouse over a row in their matrix (``Your Earnings''), the action was highlighted in yellow color in both matrices: a row in their matrix, and a column in Player Z's matrix (``Player Z's Earnings''). By left clicking the mouse over a row it remained highlighted, and participants could unhighlight it by clicking their mouse again or clicking another row. Similarly, when participants moved their mouse over a row that corresponds to an action of Player Z in ``Player Z's Earnings,'' the row was highlighted in light green and the corresponding column was highlighted in light green in ``Your Earnings.'' Clicking the mouse over the row kept it highlighted, and clicking it again (or clicking another action) unhighlighted it.

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The ``DS'' Game

Experimental Implementation:

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The ``LG'' Game

Experimental Implementation:

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The ``MS'' Game

Experimental Implementation:

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The ``NE'' Game

Experimental Implementation:

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Additional Material

Supplement to
The Streetlight Effect in Data-Driven Exploration

Here you can find examples of the interfaces participants saw during the experiment. The experiment consisted of independent "rounds." Mimicking our theoretical framework, each round was composed of two "stages" – the two time periods over which player payoffs were calculated. Participants take the role of an individual engaged in a hunt for precious gems. There are five mountains and each of them hides one type of gem, which can only be uncovered by exploring the mountain. There are three types of gems of varying rarity and value hidden in the mountains: three topazes, one ruby, and one diamond. The diamonds are always worth more than the rubies and the rubies are always worth more than the topazes. All five players are anonymous to each other and cannot directly interact or communicate. Players select which mountain to explore sequentially, based on a random order that changes every round. A dynamic instruction element on their screen turns green and indicates that it is their turn to make a choice (otherwise they must wait). None of them has any initial private information about the location of the gems, which changes every round (but not between the first and second stage of the same round). While waiting for their turn, players can see which mountains are being selected by their co-players. When it is their turn, players choose one mountain to explore. They can pick the same or different mountain as other players and their payoff is independent of whether or not their choice has already been selected by someone else. In other words, if participants overlap in their choice of mountain, each of them still receives the entire value of the gem uncovered since payoffs are non-rival.

Participants received detailed written instructions about the experiment and watched a compulsory six-minutes video that reiterated the main instructions while also familiarizing them with the experimental platform.
You can watch the no-data treatment video here and the data treatment video here.


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No-Data Treatment

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Data Treatment with Topaz Signal

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Data Treatment with Ruby Signal

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Data Treatment with Diamond Signal

Additional Material

Supplement to
Coordination in the Network Minimum Game

Here you can find examples of the interfaces participants saw during the experiments. At the start of the experiment, groups were randomly formed and participants were randomly allocated a position within a network. Groups and positions were fixed throughout the experiment. Our experimental implementation not only indicated a participant's position within the network but also highlighted their personal "watch-list" (neighbourhood). The position was highlighted in red color and the corresponding watch-list was circled in red color as well.

You can watch 5-6 minute videos participants saw before starting the experiment. The videos explain each step (screen) of the experiment with an emphasis on the concept of neighbourhood (called "watch-lists") as well as the coordination game.

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3-player network: sparse & acyclic

Experimental implementation:

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4-Player network: sparse & acyclic

Experimental implementation:

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6-Player network: sparse & acyclic

Experimental implementation:

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Watch the video here.

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Watch the video here.

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Watch the video here.

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3-player network: sparse & cyclic

Experimental implementation:

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4-Player network: sparse & cyclic

Experimental implementation:

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6-Player network: sparse & cyclic

Experimental implementation:

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Watch the video here.

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Watch the video here.

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Watch the video here.

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6-player network:
dense & acyclic

Experimental implementation:

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6-Player network:
dense & cyclic

Experimental implementation:

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12-Player network: dense & acyclic

Experimental implementation:

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Watch the video here.

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Watch the video here.

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Watch the video here.

Additional Material

Supplement to
Bandits in the Lab

Here you can find examples of the interfaces participants saw during the game, showing the evolution of the screen over time. In the top half (third) of her screen, a participant could see their own past actions and payoffs, while the bottom half (two thirds) of the screen showed their fellow group members’ actions and payoffs. A blue (red) part of the payoff curve indicated that the player used the safe (risky) arm over the corresponding period. The x-axis represented calendar time, while the y-axis gave the player’s cumulated total earnings up to each point in time. There was no prior indication of the point in time the game would end.

All four heatmaps show the total number of fixations. The accumulated number of fixations is calculated for an entire game. Each fixation made has the same value and is indepentent of its duration. A color gradient is used to indicate the areas with more fixations (low=green to high=red).

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Strategic Treatment with 2 Players

Watch the eye-tracking video here.

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Control Treatment with 2 Players

Watch the eye-tracking video here.

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Strategic Treatment with 3 Players

Watch the eye-tracking video here.

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Control Treatment with 3 Players

Watch the eye-tracking video here.