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Utility-weighted sampling in decisions from experience
Published on Jul 28, 20152125 Views
People overweight extreme events in decision-making and overestimate their frequency. Previous theoretical work has shown that this apparently irrational bias could result from utility-weighted sampl
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Chapter list
Utility-weighted sampling in decisions from experience00:00
Extreme potential outcomes - 100:10
Extreme potential outcomes - 200:30
Extreme potential outcomes - 300:56
Extreme potential outcomes - 401:14
Extreme potential outcomes - 501:20
Expected Utility Theory - 101:22
Expected Utility Theory - 201:34
Expected Utility Theory - 301:39
Expected Utility Theory - 402:03
Expected Utility Theory - 502:19
EU can be Approximated by Sampling - 102:34
EU can be Approximated by Sampling - 202:44
EU can be Approximated by Sampling - 302:48
EU can be Approximated by Sampling - 402:57
EU can be Approximated by Sampling - 503:20
Representative sampling is dangerous - 103:41
Representative sampling is dangerous - 204:01
Representative sampling is dangerous - 304:27
Representative sampling is dangerous - 404:42
Representative sampling is dangerous - 504:46
Representative sampling is dangerous - 605:27
Utility estimation by importance sampling - 105:48
Utility estimation by importance sampling - 206:06
Utility estimation by importance sampling - 306:16
Utility estimation by importance sampling - 406:32
Utility estimation by importance sampling - 506:43
Utility estimation by importance sampling - 606:59
Utility estimation by importance sampling - 707:01
Utility estimation by importance sampling - 807:31
Answer: Utility-Weighted Sampling (UWS) - 107:38
Answer: Utility-Weighted Sampling (UWS) - 207:59
Decisions from Experience (Ludvig, et al., 2014) - 108:16
Decisions from Experience (Ludvig, et al., 2014) - 208:51
Decisions from Experience (Ludvig, et al., 2014) - 308:56
Decisions from Experience (Ludvig, et al., 2014) - 409:03
Decisions from Experience (Ludvig, et al., 2014) - 509:07
Decisions from Experience (Ludvig, et al., 2014) - 609:09
Inconsistent Risk Preferences Emerge from Learning09:17
UWS Can Emerge from Reward-Modulated Associative Learning - 109:58
UWS Can Emerge from Reward-Modulated Associative Learning - 210:09
UWS Can Emerge from Reward-Modulated Associative Learning - 310:13
UWS Can Emerge from Reward-Modulated Associative Learning - 410:16
UWS Can Emerge from Reward-Modulated Associative Learning - 510:33
UWS Can Emerge from Reward-Modulated Associative Learning - 610:45
UWS Can Emerge from Reward-Modulated Associative Learning - 711:14
UWS Can Emerge from Reward-Modulated Associative Learning - 811:14
Learning Rule Convergences to Utility-Weighted Sampling - 111:29
Learning Rule Convergences to Utility-Weighted Sampling - 211:46
Efficient coding (Summerfield & Tsetsos, 2015) - 111:58
Efficient coding (Summerfield & Tsetsos, 2015) - 212:09
Efficient coding (Summerfield & Tsetsos, 2015) - 312:17
Model fitting12:38
UWS captures that people learn to overweight extreme outcomes - 112:58
UWS captures that people learn to overweight extreme outcomes - 213:07
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014) - 113:56
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014)- 214:18
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014) - 314:25
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014) - 414:31
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014) - 514:37
Utility-Weighted Sampling Captures Memory Biases (Madan et al. 2014) - 614:38
Biased Beliefs Predict Risk Seeking - 114:41
Biased Beliefs Predict Risk Seeking - 214:51
Biased Beliefs Predict Risk Seeking - 314:52
Biased Beliefs Predict Risk Seeking - 414:53
Conclusions - 115:01
Conclusions - 215:04
Conclusions - 315:14
Conclusions - 415:22
Conclusions - 515:44
Thank You15:57