Abstract: Recent research in psychology has identified a promising lie detection method. In the Verifiability Approach, investigators make use of the insight that truth-tellers are forthcoming in providing specific, verifiable details where liars sometimes prefer to remain vague. We provide a strategic analysis of the interaction between a speaker who wants to convince an investigator of his innocence and an investigator who prefers to find out the truth. The investigator can check the statement of the speaker at some cost. The examination yields informative but imperfect evidence. If the investigator decides that the speaker is lying, she adds an obstruction penalty in case her investigation suggested that the statement was a lie. We rank the equilibria of the game in terms of aggregate welfare. Our results reveal that the highest welfare is achieved if the obstruction penalty exceeds a minimum threshold that is needed to make speech informative. Surprisingly, increasing the obstruction penalty beyond this threshold decreases welfare.
Keywords: Lie detection, Verifiability approach, Strategic information revelation
JEL codes: C72, D01, D82, K14
Presented at: Annual Conference of the European Association of Law and Economics (Tel Aviv, 2019), Annual Conference of the European Association of Psychology and Law (Santiago de Compostela, 2019), Statistics and Actuarial-Finance Mathematics Seminar (Samos, 2018), CBESS-CeDEx CREED Annual Meeting (Norwich, 2018)
A typical finding in experimental cheap talk games is overcommunication. Senders are more truthful and receivers are more credulous that game theory predicts. I test whether overcommunication is observed because people are more familiar with common interest environments where such behavior is optimal. To this end, I use an experiment to establish causality between past experience and behavior in a new environment. Subjects play repeated sender-receiver games with random rematching in two stages of 30 rounds each. My design varies two dimensions, Past and Future. Past varies whether the first stage is with aligned preferences (b=0) or with conflicting (b=2). Future varies whether the environment in the second stage changes permanently (30 rounds with b=1 on stage two) or not (20 rounds with the same bias as first stage and 10 rounds with b=1 in random order on stage two). I compare behavior in the second stage across treatments.
To facilitate computations of equilibria in all treatments, I made a programm in Python. The code is available upon request. You can read more about it in the blog entry here.
Abstract: We test whether anchoring affects people's elicited valuations for a bottle of wine in individual decision making and in markets. We anchor subjects by asking them if they are willing to sell a bottle of wine for a transparently uninformative random price. We elicit subjects' Willingness-To-Accept for the bottle before and after the market. Subjects participate in a double auction market either in a small or a large trading group. The variance in subjects' Willingness-To-Accept shrinks within trading groups. Our evidence supports the idea that markets have the potential to diminish anchoring effects. However, the market is not needed: our anchoring manipulation failed in a large sample. In a concise meta-analysis, we identify the circumstances under which anchoring effects of preferences can be expected.Keywords: Anchoring, Replication, Market, Experiment
Abstract: The purpose of this study is the statistical analysis of rainfall events to explore patterns and dependencies that would allow the generalization in cases of missing or truncated data. More specifically, in this paper we estimate intensity-duration-frequency (IDF) curves, which are widely used to model rainfall. We use data from a meteorological station in Eresos, Greece and estimate the parameters of the model for fixed return periods. Sensitivity analysis is conducted to check whether the estimates are optimal. Finally, a more general model is applied that allows for simultaneous modeling of rainfall duration, intensity and frequency (via return periods).Keywords: Rainfall modeling, Non–linear regression, Intensity-duration-frequency curves, Sensitivity analysis, Monte Carlo simulation