Web Analytics
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.

More precisely, I'm interested in 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 I teach Decision Theory & Game Theory.

Publications
Working Papers
Work in Progress
Courses Taught
Research

Publications

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

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 / HUJI) & Terri Kneeland (UCL). February 2023

In the leading model of bounded rationality in games, each player best-responds to their belief that other players reason to some finite level. We propose a novel behavior that reveals the player’s belief that while other players are rational, their behavior may be outside the iterative reasoning model. 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 | Online appendix | The diagnostic games

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

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

Non-Parametric Identification and Testing of Quantal Response Equilibrium
with Ryan Webb (U of T) & Erhao Xie (BOC). March 2024

This paper studies the falsifiability and identification of Quantal Response Equilibrium (QRE) when each player's utility and error distribution are relaxed to be unknown non-parametric functions. Using variations of players' choices across a series of games, we first show that both the utility function and the distribution of errors are non-parametrically over-identified. This over-identification result further suggests a straightforward testing procedure for QRE which achieves the desired type-1 error and maintains a small type-2 error. To apply this methodology, we conduct an experimental study of the matching pennies game. Our non-parametric estimates strongly reject the conventional Logit choice probability. Moreover, when the utility and the error distribution are sufficiently flexible and heterogeneous, the quantal response hypothesis cannot be rejected for 70% of participants. However, strong assumptions such as risk neutrality, logistically distributed errors, and homogeneity lead to substantially higher rejection rates.

Go to paper | Omitted proofs | Testable implications | Comparative statics | Proofs: generalizations & extensions | Online appendix

Coordination in the Network Minimum Game
with Hongyi Li (UNSW). March 2024

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 / HUJI) & 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.

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

Coming soon

We experimentally investigate intertemporal preferences under uncertainty. Our novel design allows the direct comparison of intertemporal preferences for certain, risky, and ambiguous future monetary rewards using choice lists. The results of our experiments suggest a significant impact of risk and ambiguity on time preferences: there is a lower incidence of present bias and a higher incidence of stationarity for uncertain payments compared to certain payments. Further, present bias for certain payments is correlated with static ambiguity aversion. We also investigate possible contamination of the elicited time preferences for immediate certain payments from the choice lists. This robustness experiment consists of a single binary choice problem and shows that present bias might even be underestimated using choice lists.

Teaching

Courses

Decision theory is the mathematical study of strategies for optimal decision-making between options involving different risks or expectations of gain or loss depending on the outcome. 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 decision and 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 decision and game theory and how to use them to understand economic, social, and political phenomena. Through making decisions in in-class experiments students experience many different strategic situations first hand. In this course, we cover the following topics:

  • Decisions under uncertainty
  • 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

The current syllabus can be found here.

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.

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.

HTML5 Bootstrap Template by colorlib.com

The ``DS'' Game

Experimental Implementation:

HTML5 Bootstrap Template by colorlib.com

The ``IR'' Game

Experimental Implementation:

HTML5 Bootstrap Template by colorlib.com
HTML5 Bootstrap Template by colorlib.com
HTML5 Bootstrap Template by colorlib.com

The ``MS'' Game

Experimental Implementation:

HTML5 Bootstrap Template by colorlib.com

The ``NE'' Game

Experimental Implementation:

HTML5 Bootstrap Template by colorlib.com
HTML5 Bootstrap Template by colorlib.com


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.


HTML5 Bootstrap Template by colorlib.com

No-Data Treatment

HTML5 Bootstrap Template by colorlib.com

Data Treatment with Topaz Signal

HTML5 Bootstrap Template by colorlib.com

Data Treatment with Ruby Signal

HTML5 Bootstrap Template by colorlib.com

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.

HTML5 Bootstrap Template by colorlib.com

3-player network: sparse & acyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

4-Player network: sparse & acyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

6-Player network: sparse & acyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

3-player network: sparse & cyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

4-Player network: sparse & cyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

6-Player network: sparse & cyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

6-player network:
dense & acyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

6-Player network:
dense & cyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

12-Player network:
dense & acyclic

Experimental implementation:

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

Watch the video here.

HTML5 Bootstrap Template by colorlib.com

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).

HTML5 Bootstrap Template by colorlib.com

Strategic Treatment with 2 Players

Watch the eye-tracking video here.

HTML5 Bootstrap Template by colorlib.com

Control Treatment with 2 Players

Watch the eye-tracking video here.

HTML5 Bootstrap Template by colorlib.com

Strategic Treatment with 3 Players

Watch the eye-tracking video here.

HTML5 Bootstrap Template by colorlib.com

Control Treatment with 3 Players

Watch the eye-tracking video here.