Giovanni Ponti

Fake news on the social medias are one of the primary concerns for every functional contemporary democracy. In this paper we present the evidence of a field experiment in which we challenge a community of undergraduate students with a prize contest, Cazamentiras (literally, Liesbusters), where students are exposed, for 8 consecutive weeks, of a total of 60 items selected by well established International factcheckers. Three surveys complement the contest data, allowing us to dwell into each participant’s individual characteristics on various domains, from socio-demographics to political inclinations, from psychological traits to economic preferences. We find that, in general, our subjects are more susceptible to the “truth bias”, as the amount of false news classified as true is roughly twice as large as the amount of true news classified as false. Among our main results, more reflective subjects perform better in both on political and non political questions, whereas socio-demographics are very poor predictors of subjects’ performance. In the case of political news, subjects’ political leanings have an impact on performance, as well as the “political distance” between subjects and the news source. News characteristics are also important, in that, people seem to perform better when the news are complemented with an image and when the inaccurate information is clearly malicious.

We report the evidence of a multi-stage lab experiment on individual decision making under ambiguity, where the latter is characterized by the (partial or) absence of information on some monetary values in the support of the lottery distributions. This complements the standard treatment of uncertainty where decision makers know the monetary prizes, but probabilities are uncertain. We use both a structural and a non-structural approach when analyzing subjects’ behavior under risk, compound risk, and outcome ambiguity. Our main finding is that subjects are risk-averse and ambiguity lovers in that they evaluate more optimistically uncertain payoffs under ambiguity compared to compound risk. We also study how subjects evaluate scenarios with uncertain outcomes: 60% of choices are consistent with the Expected Utility paradigm, while 40% of them are better described by a heuristic we label as “naïve,” in which the order of integration of Expected Utility is reversed (that is, they first form a point estimate of the uncertain payoffs, and then they evaluate the lotteries’ expected utility). Finally, we also find that risk and ambiguity aversion are positively correlated.

We provide the theoretical foundations for a new estimation algorithm that non-parametrically infers higher-order beliefs in generalized guessing games with heterogeneous interactions within an extended level-k framework. The algorithm takes the strategic dependencies of the game and subjects' choices as input and returns a detailed histogram (a 'pseudo-spectrogram') of seeds that represent population beliefs about the behavior of level-0 players. As a by-product, the algorithm also returns the estimated population composition of reasoning levels. Estimating individual seeds in a highly parametrized model without requiring strong distributional assumptions is made possible by incorporating the game-theoretical structure into a mixture model. The main contributions are as follows. First, we study the equilibrium properties of generalized guessing games and provide an ordinal (visual) characterization for uniqueness. Second, within the level-k model, our key theoretical results establish conditions on the subjective beliefs or the game structure so that the population distributions of level-k choices and the population distribution of seeds are alike. These results are obtained without any distributional assumptions on the seeds. We also present a central limit result that supports the use of parametric gaussian approaches often used in the literature. Third, on the basis of the theoretical results, we construct a new non-parametric maximum likelihood estimation algorithm that fully identifies the seed pattern. Fourth, we apply the algorithm to existing experimental data. It is found that seeds cluster around a few focal points and that a few seeds are able to explain a high percentage of observed behavior. Finally, our theoretical results can also be useful in the design of laboratory guessing games with good estimation properties.

We study how the ratio between the length of the second and fourth digit (2D:4D) correlates with choices in social and risk preferences elicitation tasks by building a large dataset from five experimental projects with more than 800 subjects. Our results confirm the recent literature that downplays the link between 2D:4D and many domains of economic interest, such as social and risk preferences. As for the former, we find that social preferences are significantly lower when 2D:4D is above the median value only for subjects with low cognitive ability. As for the latter, we find that a high 2D:4D is not correlated with the frequency of subjects' risky choices.

Experimental evidence suggests that the frequency with which individuals get feedback information on their investments has an effect on their risk-taking behavior. In particular, when they are given information sufficiently often, they take less risks compared with a situation in which they are informed less frequently. We find that this result still holds when subjects do not know the probabilities of the lotteries they are betting upon. We also detect significant gender effects, in that the frequency with which information is disclosed mostly affects male betting behavior, and that males become more risk-seeking after experiencing a loss.