Pedestrians moving in dark: Balancing measures and playing games on lattices

We present two conceptually new modeling approaches aimed at describing the motion of pedestrians in obscured corridors: * a Becker-D\"{o}ring-type dynamics * a probabilistic cellular automaton model. In both models the group formation is affected by a threshold. The pedestrians are supposed to have very limited knowledge about their current position and their neighborhood; they can form groups up to a certain size and they can leave them. Their main goal is to find the exit of the corridor. Although being of mathematically different character, the discussion of both models shows that it seems to be a disadvantage for the individual to adhere to larger groups. We illustrate this effect numerically by solving both model systems. Finally we list some of our main open questions and conjectures

Optimal instructional policies based on a random-trial incremental model of learning

The random-trial incremental (RTI) model of human associative learning proposes that learning due to a trial where the association is presented proceeds incrementally, but with a certain probability, constant across trials, no learning occurs due to a trial. Based on RTI, identifying a policy for sequencing presentation trials of different associations for maximizing overall learning can be accomplished via a factored Markov decision process (MDP). For both finite and infinite horizons and a quite general structure of costs and rewards, a policy that on each trial presents an association that leads to the maximum expected immediate net reward is optimal.</p

New tools for decision analysts

This paper presents psychological research that can help people make better decisions. Decision analysts typically: 1) elicit outcome probabilities; 2) assess attribute weights; and 3) suggest the option with the highest overall value. Decision analysis can be challenging because of environmental and psychological issues. Fast and frugal methods such as natural frequency formats, frugal multiattribute models, and fast and frugal decision trees can address these issues. Not only are the methods fast and frugal, but they can also produce results that are surprisingly close to or even better than those obtained by more extensive analysis. Apart from raising awareness of these findings among engineers, the authors also call for further research on the application of fast and frugal methods to decision analysi

Entscheidungsheuristiken in Gruppen [Heuristics in group decision-making]

Es wird ein Forschungsansatz vorgestellt, der zwei Forschungstraditionen miteinander verknüpft: den kognitionspsychologischen Ansatz der „Simple Heuristics” und die sozialpsychologische Forschung zur Informationsverarbeitung in Gruppen. Die sozialpsychologische Gruppenforschung hat sich intensiv mit der Frage beschäftigt, wie die Informations- und Wissensverteilung das Entscheidungsverhalten in Gruppen beeinflusst. Diese Untersuchungen legen nahe, dass die Mitglieder einer Gruppe einen möglichst vollständigen Informationsaustausch betreiben sollten, da nur so das Wissenspotential in einer Gruppe genutzt werden kann. Der vorliegende Ansatz zeigt dazu eine Alternative auf: Anhand zweier frugaler Entscheidungsheuristiken – der Rekognitionsheuristik und der Take The Best Heuristik – wird veranschaulicht, welchen Gewinn Gruppen aus der Beschränkung der verfügbaren Informationsmenge ziehen können und an welche Informationsumgebungen die entwickelten Gruppenheuristiken angepasst sind

Categorization with limited resources: A family of simple heuristics

In categorization tasks where resources such as time, information, and computation are limited, there is pressure to be accurate, and stakes are high–as when deciding if a patient is under high risk of having a disease or if a worker should undergo retraining–, it has been proposed that people use, or should use, simple adaptive heuristics. We introduce a family of deterministic, noncompensatory heuristics, called fast and frugal trees, and study them formally. We show that the heuristics require few resources and are also relatively accurate. First, we characterize fast and frugal trees mathematically as lexicographic heuristics and as noncompensatory linear models, and also show that they exploit cumulative dominance (the results are interpreted in the language of the paired comparison literature). Second, we show, by computer simulation, that the predictive accuracy of fast and frugal trees compares well with that of logistic regression (proposed as a descriptive model for categorization tasks performed by professionals) and of classification and regression trees (used, outside psychology, as prescriptive models)

One-reason decision-making: Modeling violations of expected utility theory

Decision making, EVT, EUT, St. Petersburg Paradox,