Tamás Csermely, MSc, teaching Managerial Economics in the master’s IML program at Lauder Business School, recently had his article “How to reveal people’s preferences: Comparing Time Consistency and Predictive Power of Multiple Price List Risk Elicitation Methods” published in the Journal of Risk and Uncertainty. Together with his research partner, Mag. Alexander Rabas, he conducted a holistic investigation of the multiple price list (MPL) in order to measure and classify people’s risk preferences. In an interview, Csermely reveals why this is an important topic in business and how it influences managerial everyday decision making.

What is your recently published article about?

Csermely: The article is about preferences and the corresponding actions we take. At first sight, this sounds complex, so let me explain with an example: Each of us likes different things in the real world. Some prefer drinking apple juice, others like orange juice better. Our choice between the juices is driven by our preferences. In the last decades, behavioral economists and psychologists have shed light on the connection between preferences and several behavioral patterns in the real world. For example, researchers showed the link between personal risk attitude and various factors such as how one invests, how much inventory companies hold or whether one applies for particular jobs or not, etc. Our recently published article focuses on whether these measures can be measured in an objective and unbiased way.

Why is researching this field important?

Csermely: Preferences drive our behavior in everyday life. Our article finds that the way we measure such preferences has a huge impact on what the results are. We conducted an experiment on risk preferences with 10 measurement methods that, in theory, should lead to the same result. Interestingly, we found that these different preference measurement methods lead to completely different results for the same people. Even more interestingly, we found that the same method for the very same person often leads to contradicting results when the test is done 2 times with only a 30-minute difference!

Of course, this is problematic if such noisy preference measures are used to explain real-world behavior, since the connection between measured preferences and the observed actions can be questionable. Our contribution pinpoints how exactly risk attitude should be measured.

Who can benefit from this research?

Csermely: If I were an idealist, I would say everyone. The realist in me says that only a narrow academic community would read – maybe the introduction and the conclusion of – the paper. My take is that researchers who deal with preference elicitation will definitely benefit. Unfortunately, academic articles in premier economics journals are usually very technical and relatively hard to understand for the general public. Nonetheless, there are several entertaining books on behavioral economics that the general public can learn a lot from: Dan Ariely: Predictibly irrational; Rolf Dobelli: The art of thinking clearly; Daniel Kahneman: Thinking, fast, and slow; Simon Kuper & Stefan Szymanski: Soccernomics; Steven Levitt & Stephen J. Dubner: Freakonomics; Steven Levitt & Stephen J. Dubner: SuperFreakonomics; Julia Shaw: The memory illusion; Nate Silver: The signal and the noise: Why so many predictions fail – but some don’t; Paula Szuchman & Jenny Anderson: Spousonomics; Richard Thaler & Cass R. Sunstein: Nudge and also the works by Nassim Nicholas Thaleb, to name just a few.

What does your future research focus on?

Csermely: All of my research focuses on experimental and empirical investigations. I have an upcoming paper that focuses on how the feedback we receive on the actual demand biases one’s decisions when it comes to how much product (s)he orders in the next period. Even though people know that the demand distribution remained the same across periods in the game, they still chase the demand they had observed in the previous period. So, this is a story of biased probability judgment, namely how past outcomes lead to availability heuristics and biased beliefs, which, in the end, shape future business-related decisions.

I am also working on further operations- and supply chain-related projects. These working papers cover business settings such as the way people purchase from a slow, inexpensive and a fast, expensive supplier (dual sourcing) and how warehouses decide to allocate a limited number of products between retailers (inventory rationing, capacity allocation). On a final note, I am interested in behavioral economics, behavioral operations management in a broad sense. I also like to show the power of biases and decisions to my students with interactive situations and games.

Did you use any of the research findings to change your own risk perception? (e.g. in the “Who wants to be a Millionaire Show?”)

Csermely: I think that my risk perception remained more or less the same, but at least now I know some of the underlying factors behind it. I am also convinced that having dealt with decision theory and personal biases for 7 years helped me a lot to get around in this world and to see through people. This is very useful both in everyday life and my day-to-day job that involves leading purchasing projects and often negotiations.

In the “Who Wants To Be A Millionaire Show”, anxiety and fatigue makes you forget even your own name in that particular chair, however, I guess I am less risk-seeking than I was 10 years ago when the show was recorded. I just started my answer with claiming that my risk perception remained the same, yet I contradict myself in 5 sentences! This proves that I am still biased and I have a lot to learn.

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