The use of recognition in group decision‐making

Reimer, Torsten; Katsikopoulos, Konstantinos V.

doi:10.1207/s15516709cog2806_6

Cited by 83 publications

(9 citation statements)

References 13 publications

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“…Our set of simple models demonstrate that both aggregated and expert rules can enable accurate collective solutions to challenging problems [9], as this has also been found in human group decision-making [27], [28]. Both choice rules we have considered do not necessarily require advanced cognitive abilities, but only that individuals have the potential to acquire information through social interaction, and respond positively to those who possess pertinent information [9], [12] (Models 1 and 2), or update their choice rules based on the success of the previous decisions [29] (Model 3).…”

Section: Resultssupporting

confidence: 55%

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

Katsikopoulos

King

2010

PLoS ONE

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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 collective's 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.

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Section: Resultssupporting

confidence: 55%

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

Katsikopoulos

King

2010

PLoS ONE

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“…Several demonstrations have shown that having heard of a larger number of objects is sometimes associated with lower inferential accuracy, both for individual decision makers (e.g., Goldstein and Gigerenzer, 2002) and groups (Reimer and Katsikopoulos, 2004). However, reviewing 10 data sets where the recognition validity was larger than the knowledge validity – one of the conditions for the effect highlighted by Goldstein and Gigerenzer (2002) – Pachur (2010) concluded that the evidence for the effect is mixed.…”

Section: The Recognition Heuristic As a Descriptive Modelmentioning

confidence: 99%

The Recognition Heuristic: A Review of Theory and Tests

Pachur¹,

Todd²,

Martignon³

et al. 2011

Front. Psychology

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The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

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“…We can use the majority rule (which simplifies computation by just tallying yes/ no judgments rather than, for instance, weighting them according to importance of different judges) and other heuristics to make group decisions about people as potential employees, based on the distribution of information within our group (Todd et al, in press). Patterns of recognition knowledge gained by individuals via social exchange can be successfully exploited by the recognition heuristic mentioned earlier (Gigerenzer et al, 1999) to decide which of two items is larger (e.g., cities) or more successful (e.g., tennis players or stocks), and such recognition patterns are also given prominence in group decision making (Reimer & Katsikopoulos, 2004). But people do not use the recognition heuristic blindly-rather they seem to evaluate intuitively its ecological rationality for a given situation (via mechanisms that have been associated with particular neural structures-see Gigerenzer, 2007).…”

Section: Relevant Structure In Environmentsmentioning

confidence: 99%

Environments That Make Us Smart

Todd

Gigerenzer

2007

Curr Dir Psychol Sci

294

161

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Traditional views of rationality posit generalpurpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the ''adaptive toolbox'' of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.

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The use of recognition in group decision‐making

Cited by 83 publications

References 13 publications

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

The Recognition Heuristic: A Review of Theory and Tests

Environments That Make Us Smart

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