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What is the role of recognition in decision making?

What is the role of recognition in decision making?

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What is the role of recognition in decision making?

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  1. What is the role of recognition in decision making? Ben Newell University College London & Centre for Economic Learning & Social Evolution Acknowledgements: David Shanks, Nicola Weston, Tim Rakow Funding: ESRC, Leverhulme Trust

  2. Role of recognition in a cue-learning task • Previous work examined empirical evidence for building blocks of fast & frugal heuristics (e.g., search, stopping, decision rules) in menu-based tasks • Natural extension – examine evidence for fundamental ‘building block’ – the use of recognition

  3. What do we mean by recognition? • Distinction between the truly novel and the previously experienced • E.g. nonwords – “prache”, “elbonics” • Repetition of nonwords makes them recognisable • How much ‘weight’ is placed on simple recognition?

  4. RH = Recognition High (recognition best predictor of company performance) RL = Recognition Low (recognition poorest predictor of company performance) NR = No Recognition (Free advisor informational equivalent of RH)

  5. Choices in accord with recognition Predictions: “special” RH = RL = 1.0; “consistent” RH > RL

  6. Advice Purchase Predictions: “special” RH = RL (=0) < NR; “consistent” NR = RH < RL

  7. Compensatory use of cues Evidence for compensatory cue use in all conditions, most in RL

  8. Conclusions • Recognition information not ascribed “special status” in cue learning task • Treated as ‘just another cue’ in the environment (cf.,PROBEX Juslin & Persson 2002) • What about inferences from memory – do these rely on a ‘different sort of recognition’?

  9. Recognition, Availability, Familiarity……. • Powerful influences on inferences from memory • Availability Heuristic (Kahneman & Tversky) • “Overnight Fame” effect (Jacoby) • Recognition Heuristic (Goldstein & Gigerenzer) Availability (ease of recall) No Recognition Recognition Recognition +

  10. Recognition Heuristic • Adaptive, non-compensatory, “all or nothing” use of recognition – “no other information is searched for” • Cities task with football team information • When is such a rule applied? What are the consequences…..?

  11. “Paying for the name…….”

  12. Paying for the name….. • Hoyer & Brown (1990) • 3 brands of peanut butter, • “Aware group”:1 known, 2 unknown brands • 5 trials, opportunity to sample after each choice • Support for use of brand recognition in choice of peanut butter (DVD’s, computers, cars…….??)

  13. Paying for the name….. • Hoyer & Brown (1990) contd…. • Comparison with “No Awareness” group • Significantly more sampling of brands in No Awareness group AND • Awareness/quality-difference manipulation showed: • Reliance on Brand Awareness heuristic led to decreased search and final choice of inferior alternative

  14. “A good name is better than riches” (?) • Borges et al. (1999) – can “ignorance” beat the stock market? • 180 German lay-people recognition of German stocks • 6 month return on DAX 30: Dec 1996 – Jun 1997 • Result replicated in 6 out of 8 tests • Conclusion – ignorance can beat the stock market or big firms do well in strong bull (up) markets?

  15. “A good name is better than riches” (?) • Boyd (2001) – test in a down or ‘bear market’ • 184 US students recognition of 111 companies randomly selected from Standard & Poor’s 500 • 6 month return: June 2000 – December 2000 • No evidence to support use of recognition heuristic in a ‘bear’ market • Borges et al result a ‘big firm’ effect?

  16. “A good name is better than riches” (?) • Rakow (2002) – further test in a strong market • 53 UK students recognition of 30 companies in Italian Mib 30 • 8 week return: October – December 2002 • Compared recognition portfolios, anti-recognition portfolios and expert portfolio with market index • Only 7 out of 53 recognition portfolios outperformed market index • How robust is recognition heuristic as an investment tool?

  17. When will recognition be accurate…? • It depends on the domain….. f(n) = 2(n / N) (N - n / N -1) + (N – n / N) (N – n – 1 / N – 1) ½ + (n / N) (n – 1 / N – 1) f(n) = proportion correct inferences  = recognition validity  =knowledge validity N = reference class of objects n = recognized objects (fast and frugal?)

  18. Deliberate or automatic? • Automatic ‘feeling of familiarity’ + deliberate application of heuristic? • “I recognise it so I’ll choose it” – deliberate selection and use of a heuristic from the toolbox? (cf., Kahneman & Frederick, 2002) • Recognition + “relevance check”. Enron?

  19. Conclusions • Cue learning: recognition treated like other cues in the environment • Consumer research:Exploitation of reliance on recognition • Stock market investment: ‘big firm’ rather than recognition effect – how generalisable?

  20. Where to next? • Further tests of use of recognition in memory-based inference • Cost/benefit effects on use of recognition • Discussion of adaptive/maladaptive use of recognition • Further specification of the domains in which the ‘recognition heuristic’ applies