Konstantinos V. Katsikopoulos

Risk attitude reversals in drivers' route choice when range of travel time information is provided

Automobile drivers were recently found to be risk averse when choosing among routes that had an average travel time shorter than the certain travel time of a route considered as a reference. Conversely, drivers were found to be risk seeking when choosing among routes that had an average travel time longer than the certain travel time of the reference route. In a driving simulation study in which the reference route had a range of travel times, this pattern was replicated when the reference range was smaller than the ranges of the available routes. However, the pattern was reversed when the reference range was larger than the ranges of the available routes. We recently proposed a simple heuristic model that fit the relatively complex data quite well. Actual or potential applications of this research include the design of variable message signs and of route choice support systems.

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 analysis

THE FRAMING OF DRIVERS' ROUTE CHOICES WHEN TRAVEL TIME INFORMATION IS PROVIDED UNDER VARYING DEGREES OF COGNITIVE LOAD

In two experiments, participants chose between staying on a main route with a certain travel time and diverting to an alternative route that could take a range of travel times. In the first experiment, travel time information was displayed on a sheet of paper to participants seated at a desk. In the second experiment, the same information was displayed in a virtual environment through which participants drove. Overall, participants were risk-averse when the average travel time along the alternative route was shorter than the certain travel time of the main route but risk-seeking when the average travel time of the alternative route was longer than the certain travel time along the main route. In the second experiment, in which cognitive load was higher, participants simplified their decision-making strategies. A simple probabilistic model describes the risk-taking behavior and the load effects. Actual or potential applications of this research include the development of efficient travel time information systems for drivers.

The Robust Beauty of Ordinary Information.

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues that do not entail individual learning. Then we propose and test the thesis that when orders are learned individually, peoples necessarily limited knowledge will curtail computational complexity while also achieving robustness. Using computer simulations, we compare the performance of the take-the-best heuristic--with dichotomized or undichotomized cues--to benchmarks such as the naïve Bayes algorithm across 19 environments. Even with minute sizes of training sets, take-the-best using undichotomized cues excels. For 10 environments, we probe peoples intuitions about the direction of the correlation between cues and criterion. On the basis of these intuitions, in most of the environments take-the-best achieves the level of performance that would be expected from learning cue orders from 50% of the objects in the environments. Thus, ordinary information about cues--either gleaned from small training sets or intuited--can support robust performance without requiring Herculean computations.

Markov decision processes with delays and asynchronous cost collection

Markov decision processes (MDPs) may involve three types of delays. First, state information, rather than being available instantaneously, may arrive with a delay (observation delay). Second, an action may take effect at a later decision stage rather than immediately (action delay). Third, the cost induced by an action may be collected after a number of stages (cost delay). We de rive two results, one for constant and one for random delays, for reducing an MDP with delays to an MDP without delays, which differs only in the size of the state space. The results are based on the intuition that costs may be collected asynchronously, i.e., at a stage other than the one in which they are induced, as long as they are discounted properly.

Swarm intelligence in animal groups: when can a collective out-perform an expert?

An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their ‘collective cognition’, or (ii) interaction with conspecifics can allow individuals to follow specific ‘leaders’, those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions – where individuals are able to consider the success of previous decision outcomes – the collectives aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated.

Naturalistic Heuristics for Decision Making

Over the last 20 years, both naturalistic decision making and fast and frugal heuristics programs have radically broken with mainstream decision science, moving beyond the confines of artificial tasks and safe academic laboratories. We document commonalities of these programs and discuss ways in which a synthesis could contribute to a more relevant, precise, predictive, and effective decision science. We begin by reviewing the common roots and philosophies of the two programs, such as their respect for the capable decision maker and their acknowledgment of the importance of task ecology. We then identify four specific areas of synergetic potential, including ecological rationality and metacognition. Our review culminates in a case study of naturalistic heuristics based on a particular class of fast and frugal heuristics. These fast and frugal trees provide examples of effective, well-specified decision-making algorithms applied in a naturalistic domain: emergency medical diagnosis. By leveraging the strengths of each program, we point out some of the ways in which more sustainable progress can be fostered on issues that matter the most—for example, decisions that save and transform lives.

Bounded rationality : The two cultures

Research on bounded rationality has two cultures, which I call ‘idealistic’ and ‘pragmatic’. Technically, the cultures differ on whether they (1) build models based on normative axioms or empirical facts, (2) assume that peoples goal is to optimize or to satisfice, (3) do not or do model psychological processes, (4) let parameters vary freely or fix them, (5) aim at explanation or prediction and (6) test models from one or both cultures. Each culture tells a story about peoples rationality. The story of the idealistic culture is frustrating, with people in principle being able to know what they should do, but in practice systematically failing to do it. This story makes one hide in books for intellectual solace or surrender to the designs of someone smarter. The story of the pragmatic culture is empowering: If people are educated to use the right tool in the right situation, they do well.

Taking Uncertainty Seriously: Simplicity versus Complexity in Financial Regulation

Distinguishing between risk and uncertainty, this paper draws on the psychological literature on heuristics to consider whether and when simpler approaches may outperform more complex methods for modelling and regulating the financial system. We find that: (i) simple methods can sometimes dominate more complex modelling approaches for calculating banks’ capital requirements, especially if limited data are available for estimating models or the underlying risks are characterised by fat-tailed distributions; (ii) simple indicators often outperformed more complex metrics in predicting individual bank failure during the global financial crisis; and (iii) when combining information from different indicators to predict bank failure, ‘fast-and-frugal’ decision trees can perform comparably to standard, but more information-intensive, regression techniques, while being simpler and easier to communicate.

An Evaluation of the Pugh Controlled Convergence Method

This paper evaluates a method known as Pugh Controlled Convergence and its relationship to recent developments in design theory. Computer executable models are proposed simulating a team of people involved in iterated cycles of evaluation, ideation, and investigation. The models suggest that: 1) convergence of the set of design concepts is facilitated by the selection of a strong datum concept; 2) iterated use of an evaluation matrix can facilitate convergence of expert opinion, especially if used to plan investigations conducted between matrix runs; and 3) ideation stimulated by the Pugh matrices can provide large benefits both by improving the set of alternatives and by facilitating convergence. As a basis of comparison, alternatives to Pugh’s methods were assessed such as using a single summary criterion or using a Borda count. The models we developed suggest that Pugh’s method, under a substantial range of assumptions, results in better design outcomes than those from these alternative procedures.© 2007 ASME