Naturally-occurring sleep choice and time of day effects on p-beauty contest outcomes

We explore the behavioral consequences of sleep loss and time-of-day (circadian) effects on a particular type of decision making. Subject sleep is monitored for the week prior to a decision experiment, which is then conducted at 8 a.m. or 8 p.m. A validated circadian preference instrument allows us to randomly assign subjects to a more or less preferred time-of-day session. The well-known p-beauty contest (a.k.a., the guessing game) is administered to examine how sleep loss and circadian mismatch affect subject reasoning and learning. We find that the subject responses are consistent with significantly lower levels of iterative reasoning when ‘sleep deprived’ or at non-optimal times-of-day. A non-linear effect is estimated to indicate that too much sleep also leads to choices consistent with lower levels of reasoning, with an apparent optimum at close to 7 hours sleep per night. However, repeated play shows that sleep loss and non-optimal times-of-day do not affect learning or adaptation in response to information feedback. Our results apply to environments where anticipation is important, such as in coordination games, stock trading, driving, etc. These findings have important implications for the millions of adults considered sleep deprived, as well as those employed in shift work occupations. Key Words:

Teaching Nash Equilibrium and Strategy Dominance: A Classroom Experiment on the Beauty Contest

The aim of this investigation is to display how the use of classroom experiments may be a good pedagogical tool to teach the Nash equilibrium (NE) concept. The basic game for our purposes is a repeated version of the Beauty Contest Game (BCG), a simple guessing game whose repetition lets students react to other players’ choices and to converge iteratively to the equilibrium solution. We performed this experiment with undergraduate students without any previous knowledge about game theory. After four rounds, we observed in all groups a clear decreasing tendency in the average chosen number. So, our findings prove that, by playing a repeated BCG, students quickly learn how to reach the NE solution.Classroom Experiments, Beauty Contest Game, Teaching, Nash Equilibrium.

Positive expectations feedback experiments and number guessing games as models of financial markets (revised version of WP 08-07)

In repeated number guessing games choices typically converge quickly to the Nash equilibrium. In positive expectations feedback experiments, however, convergence to the equilibrium price tends to be very slow, if it occurs at all. Both types of experimental designs have been suggested as modeling essential aspects of financial markets. In order to isolate the source of the differences in outcomes we present several new treatments in this paper. We conclude that the feedback strength (i.e. the ‘p-value’ in standard number guessing games) is essential for the results. Furthermore, positive expectations feedback experiments may provide good representations of highly speculative markets while standard number guessing games model financial markets with more emphasis on dividend yield and value stocks.

Cognitive effort in the Beauty Contest Game

This paper analyzes cognitive effort in 6 different one-shot p-beauty games. We use both Raven and Cognitive Reflection tests to identify subjects' abilities. We find that the Raven test does not provide any insight on beauty contest game playing but CRT does: subjects with higher scores on this test are more prone to play dominant strategies.Beauty Contest Game, Raven, Cognitive Reflection Test

Public Speaking Catch Phrase: Reinforcing good public speaking skills through an interactive in-class gaming experience

Public Speaking Catch Phrase is an interactive in-classroom game derived out of the word guessing party game, Catch Phrase. Public Speaking Catch Phrase intends to make students aware of their communication habits and to develop and reinforce good public speaking skills. Speakers, or “clue-givers,” from two teams will alternate turns and deliver clues to get their team to say the words displayed on the electronic game device. However, students must follow “rules” promoting good public speaking practices in order to receive points. This includes maximizing metaphors and punctuation with gestures, and minimizing non-words (e.g., “um,” “uh,” “er”) and fluency disruptions (e.g., stammering, slurred articulation). This activity challenges students to focus, think quickly, and build a speaker-audience relationship in an interactive, fun, and energetic way

Can We Build Behavioral Game Theory?

The way economists and other social scientists model how people make interdependent decisions is through the theory of games. Psychologists and behavioral economists, however, have established many deviations from the predictions of game theory. In response to these findings, a broad movement has arisen to salvage the core of game theory. Extant models of interdependent decision-making try to improve their explanatory domain by adding some corrective terms or limits. We will make the argument that this approach is misguided. For this approach to work, the deviations would have to be consistent. Drawing in part on our experimental results, we will argue that deviations from classical models are not consistent for any individual from one task to the next or between individuals for the same task. In turn, the problem of finding an equilibrium strategy is not easier but rather is exponentially more difficult. It does not seem that game theory can be repaired by adding corrective terms (such as consideration of personal characteristics, social norms, heuristic or bias terms, or cognitive limits on choice and learning). In what follows, we describe new methods for investigating interdependent decision-making. Our experimental results show that people do not choose consistently, do not hold consistent beliefs, and do not in general align actions and beliefs. We will show that experimental choices are inconsistent in ways that prevent us from drawing general characterizations of an individual’s choices or beliefs or of the general population\u27s choices and beliefs. A general behavioral game theory seems a distant and, at present, unfulfilled hope

On the absorbability of the Guessing Game Theory. A Theoretical and Experimental Analysis

Theory absorption, a notion introduced by Morgenstern and Schwödiauer (1972) and further elaborated by Güth and Kliemt (2004), discusses the problem whether a theory can survive its own acceptance. Whereas this holds for strategic equilibria according to the assumptions on which they are based, the problem if theories are absorbable by at most boundedly rational decision makers is hardly discussed. Based on guessing game experiments we discuss the requirements of equilibrium theory absorption and test experimentally the effects of informing none, some or all players about how to derive equilibrium predictions.theory absorption; guessing game; p-beauty contest; individual behaviour; elimination of dominated strategies

PassGAN: A Deep Learning Approach for Password Guessing

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd