The Role of Surprise in Hindsight Bias – A Metacognitive Model of Reduced and Reversed Hindsight Bias

Hindsight bias is the well researched phenomenon that people falsely believe that they would have correctly predicted the outcome of an event once it is known. In recent years, several authors have doubted the ubiquity of the effect and have reported a reversal under certain conditions. This article presents an integrative model on the role of surprise as one factor explaining the malleability of the hindsight bias. Three ways in which surprise influences the reconstruction of pre-outcome predictions are assumed: (1) Surprise is used as direct metacognitive heuristic to estimate the distance between outcome and prediction. (2) Surprise triggers a deliberate sense-making process, and (3) also biases this process by enhancing the retrieval of surprise-congruent information and expectancy-based hypothesis testing.

Direct and indirect effects of mood on risk decision making in safety-critical workers

The study aimed to examine the direct influence of specific moods (fatigue, anxiety, happiness) on risk in safety-critical decision making. It further aimed to explore indirect effects, specifically, the potential mediating effects of information processing assessed using a goodness-of-simulation task. Trait fatigue and anxiety were associated with an increase in risk taking on the Safety-Critical Personal Risk Inventory (S-CPRI), however the effect of fatigue was partialled out by anxiety. Trait happiness, in contrast was related to less risky decision making. Findings concerning the ability to simulate suggest that better simulators made less risky decisions. Anxious workers were generally less able to simulate. It is suggested that in this safety-critical environment happiness had a direct effect on risk decision making while the effect of trait anxiety was mediated by goodness-of-simulation

A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior

Which heuristics can aid financial-decision-making?

© 2015 Elsevier Inc. We evaluate the contribution of Nobel Prize-winner Daniel Kahneman, often in association with his late co-author Amos Tversky, to the development of our understanding of financial decision-making and the evolution of behavioural finance as a school of thought within Finance. Whilst a general evaluation of the work of Kahneman would be a massive task, we constrain ourselves to a more narrow discussion of his vision of financial-decision making compared to a possible alternative advanced by Gerd Gigerenzer along with numerous co-authors. Both Kahneman and Gigerenzer agree on the centrality of heuristics in decision making. However, for Kahneman heuristics often appear as a fall back when the standard von-Neumann-Morgenstern axioms of rational decision-making do not describe investors' choices. In contrast, for Gigerenzer heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. Gigerenzer challenges Kahneman to move beyond substantiating the presence of heuristics towards a more tangible, testable, description of their use and disposal within the ever changing decision-making environment financial agents inhabit. Here we see the emphasis placed by Gigerenzer on how context and cognition interact to form new schemata for fast and frugal reasoning as offering a productive vein of new research. We illustrate how the interaction between cognition and context already characterises much empirical research and it appears the fast and frugal reasoning perspective of Gigerenzer can provide a framework to enhance our understanding of how financial decisions are made

Project management under uncertainty

Morris' (1986) analysis of the factors affecting project success and failure is considered in relation to the psychology of judgement under uncertainty. A model is proposed whereby project managers may identify the specific circumstances in which human decision-making is prone to systematic error, and hence may apply a number of de-biasing techniques

Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.System 1, Intuition, Reasoning, Control, Routines, Connectionist Model, Parallel Constraint Satisfaction

More than one way to see it: Individual heuristics in avian visual computation

Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally