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Abstract

Positive mood can affect a person’s tendency to gamble, possibly because positive mood fosters unrealistic optimism. At the same time, unexpected positive outcomes, often called prediction errors, influence mood. However, a linkage between positive prediction errors—the difference between expected and obtained outcomes—and consequent risk taking has yet to be demonstrated. Using a large data set of New York City lottery gambling and a model inspired by computational accounts of reward learning, we found that people gamble more when incidental outcomes in the environment (e.g., local sporting events and sunshine) are better than expected. When local sports teams performed better than expected, or a sunny day followed a streak of cloudy days, residents gambled more. The observed relationship between prediction errors and gambling was ubiquitous across the city’s socioeconomically diverse neighborhoods and was specific to sports and weather events occurring locally in New York City. Our results suggest that unexpected but incidental positive outcomes influence risk taking.

Keywords

decision making risk taking emotions open data

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References

Arkes

H. R.

Herren

L. T.

Isen

A. M.

(1988). The role of potential loss in the influence of affect on risk-taking behavior. Organizational Behavior and Human Decision Processes, 42, 181–193. doi:10.1016/0749-5978(88)90011-8

Barnes

G. M.

Welte

J. W.

Tidwell

M.-C. O.

Hoffman

J. H.

(2011). Gambling on the lottery: Sociodemographic correlates across the lifespan. Journal of Gambling Studies, 27, 575–586. doi:10.1007/s10899-010-9228-7

Bassi

Colacito

Fulghieri

(2013). ’O Sole Mio: An experimental analysis of weather and risk attitudes in financial decisions. The Review of Financial Studies, 26, 1824–1852. doi:10.1093/rfs/hht004

Bayer

H. M.

Glimcher

P. W.

(2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129–141. doi:10.1016/j.neuron.2005.05.020

Behrens

T. E. J.

Woolrich

M. W.

Walton

M. E.

Rushworth

M. F. S.

(2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10, 1214–1221. doi:10.1038/nn1954

Button

K. S.

Ioannidis

J. P. A.

Mokrysz

Nosek

B. A.

Flint

Robinson

E. S. J.

Munafò

M. R.

(2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365–376. doi:10.1038/nrn3475

Card

Dahl

G. B.

(2011). Family violence and football: The effect of unexpected emotional cues on violent behavior. The Quarterly Journal of Economics, 126, 103–143. doi:10.1093/qje/qjr001

Clotfelter

C. T.

Cook

P. J.

(1987). Implicit taxation in lottery finance (Working Paper No. 2246). Retrieved from the National Bureau of Economic Research Web site: http://www.nber.org/papers/w2246

Coviello

Sohn

Kramer

A. D. I.

Marlow

Franceschetti

Christakis

N. A.

Fowler

J. H.

(2014). Detecting emotional contagion in massive social networks. PLoS ONE, 9(3), Article e90315. doi:10.1371/journal.pone.0090315

10.

Crossley

T. F.

Low

Smith

(2013). Do consumers gamble to convexify? (Koç University-TÜSİAD Economic Research Forum Working Paper Series No. 1314). Retrieved from http://www.econstor.eu/handle/10419/108633

11.

Cunningham

M. R.

(1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 37, 1947–1956. doi:10.1037/0022-3514.37.11.1947

12.

Delgado

M. R.

Schiller

Phelps

E. A.

(2008). The role of the striatum in aversive learning and aversive prediction errors. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 3787–3800. doi:10.1098/rstb.2008.0161

13.

Edmans

García

Norli

. (2007). Sports sentiment and stock returns. The Journal of Finance, 62, 1967–1998. doi:10.1111/j.1540-6261.2007.01262.x

14.

Eichstaedt

J. C.

Schwartz

H. A.

Kern

M. L.

Park

Labarthe

D. R.

Merchant

R. M.

. . . Seligman

M. E. P.

(2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26, 159–169. doi:10.1177/0956797614557867

15.

Eldar

Niv

(2015). Interaction between emotional state and learning underlies mood instability. Nature Communications, 6, Article 6149. doi:10.1038/ncomms7149

16.

Evans

W. N.

Moore

T. J.

(2011). The short-term mortality consequences of income receipt. Journal of Public Economics, 95, 1410–1424. doi:10.1016/j.jpubeco.2011.05.010

17.

Galea

Ahern

Nandi

Tracy

Beard

Vlahov

(2007). Urban neighborhood poverty and the incidence of depression in a population-based cohort study. Annals of Epidemiology, 17, 171–179. doi:10.1016/j.annepidem.2006.07.008

18.

Garrett

Kamstra

M. J.

Kramer

L. A.

(2005). Winter blues and time variation in the price of risk. Journal of Empirical Finance, 12, 291–316. doi:10.1016/j.jempfin.2004.01.002

19.

Gibson

Sanbonmatsu

D. M.

(2004). Optimism, pessimism, and gambling: The downside of optimism. Personality and Social Psychology Bulletin, 30, 149–160. doi:10.1177/0146167203259929

20.

Gilovich

(1983). Biased evaluation and persistence in gambling. Journal of Personality and Social Psychology, 44, 1110–1126.

21.

Haushofer

Fehr

(2014). On the psychology of poverty. Science, 344, 862–867. doi:10.1126/science.1232491

22.

Hirshleifer

Shumway

(2003). Good day sunshine: Stock returns and the weather. The Journal of Finance, 58, 1009–1032. doi:10.1111/1540-6261.00556

23.

Højsgaard

Halekoh

(2009). doBy: Groupwise statistics, LSmeans, linear contrasts, utilities (Version 4.5-13) [Software]. Retrieved from http://CRAN.R-project.org/package=doBy

24.

Isen

A. M.

Patrick

(1983). The effect of positive feelings on risk taking: When the chips are down. Organizational Behavior and Human Performance, 31, 194–202. doi:10.1016/0030-5073(83)90120-4

25.

Johnson

D. D. P.

Fowler

J. H.

(2011). The evolution of overconfidence. Nature, 477, 317–320. doi:10.1038/nature10384

26.

Kamstra

M. J.

Kramer

L. A.

Levi

M. D.

(2003). Winter blues: A SAD stock market cycle. The American Economic Review, 93, 324–343.

27.

Kamstra

M. J.

Kramer

L. A.

Levi

M. D.

(2014). Seasonal variation in Treasury returns. Retrieved from Social Science Research Network Web site: http://papers.ssrn.com/abstract=1076644

28.

Kamstra

M. J.

Kramer

L. A.

Levi

M. D.

Wermers

(2015). Seasonal asset allocation: Evidence from mutual fund flows. Retrieved from Social Science Research Network Web site: http://ssrn.com/abstract=1907904

29.

Kuhnen

C. M.

Knutson

(2011). The influence of affect on beliefs, preferences, and financial decisions. Journal of Financial and Quantitative Analysis, 46, 605–626. doi:10.1017/S0022109011000123

30.

Love

B. C.

Kopeć

Ł.

Guest

(2015). Optimism bias in fans and sports reporters. PLoS ONE, 10(9), Article e0137685. doi:10.1371/journal.pone.0137685

31.

Mellers

B. A.

Schwartz

Ritov

(1997). Decision affect theory: Emotional reactions to the outcomes of risky options. Psychological Science, 8, 423–429. doi:10.2307/40063228

32.

New York City Department of City Planning. (2013). Primary land use tax lot output [Data set]. Retrieved from http://www.nyc.gov/html/dcp/html/bytes/dwn_pluto_mappluto.shtml

33.

Nielsen Company. (2013). Local television market universe estimates: Comparisons of 2012-13 and 2013-14 market ranks. Retrieved from http://www.nielsen.com/content/dam/corporate/us/en/docs/solutions/measurement/television/2013-2014-DMA-Ranks.pdf

34.

O’Doherty

J. P.

Dayan

Friston

Critchley

Dolan

R. J.

(2003). Temporal difference models and reward-related learning in the human brain. Neuron, 38, 329–337. doi:10.1016/S0896-6273(03)00169-7

35.

Oster

(2004). Are all lotteries regressive? Evidence from the Powerball. National Tax Journal, 57, 179–187.

36.

Perez

Ineichen

Moore

Kmiecik

Chain

George

Vignola

(2002). A new operational model for satellite-derived irradiances: Description and validation. Solar Energy, 73, 307–317. doi:10.1016/S0038-092X(02)00122-6

37.

Pfiffelmann

(2013). What is the optimal design for lottery-linked savings programmes? Applied Economics, 45, 4861–4871. doi:10.1080/00036846.2013.804168

38.

Pinheiro

J. C.

Bates

D. M.

(2000). Mixed-effects models in S and S-PLUS. New York, NY: Springer.

39.

R Development Core Team. (2015). R: A language and environment for statistical computing (Version 3.2.2) [Computer software]. Retrieved from https://www.r-project.org/index.html

40.

Roberts

E. M.

(1997). Neighborhood social environments and the distribution of low birthweight in Chicago. American Journal of Public Health, 87, 597–603.

41.

Rogers

(1998). The cognitive psychology of lottery gambling: A theoretical review. Journal of Gambling Studies, 14, 111–134. doi:10.1023/A:1023042708217

42.

Rubenstein

Scafidi

Rubinstein

(2002). Who pays and who benefits? Examining the distributional consequences of the Georgia lottery for education. National Tax Journal, 55, 223–238.

43.

Rutledge

R. B.

Skandali

Dayan

Dolan

R. J.

(2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences, USA, 111, 12252–12257. doi:10.1073/pnas.1407535111

44.

Rutledge

R. B.

Skandali

Dayan

Dolan

R. J.

(2015). Dopaminergic modulation of decision making and subjective well-being. The Journal of Neuroscience, 35, 9811–9822. doi:10.1523/JNEUROSCI.0702-15.2015

45.

Schulreich

Heussen

Y. G.

Gerhardt

Mohr

P. N.

Binkofski

F. C.

Koelsch

Heekeren

H. R.

(2014). Music-evoked incidental happiness modulates probability weighting during risky lottery choices. Frontiers in Psychology, 4, Article 981. doi:10.3389/fpsyg.2013.00981

46.

Schultz

Dayan

Montague

P. R.

(1997). A neural substrate of prediction and reward. Science, 275, 1593–1599. doi:10.1126/science.275.5306.1593

47.

Sharot

(2011). The optimism bias. Current Biology, 21, R941–R945. doi:10.1016/j.cub.2011.10.030

48.

Sharot

Korn

C. W.

Dolan

R. J.

(2011). How unrealistic optimism is maintained in the face of reality. Nature Neuroscience, 14, 1475–1479. doi.org/10.1038/nn.2949

49.

Shepperd

J. A.

McNulty

J. K.

(2002). The affective consequences of expected and unexpected outcomes. Psychological Science, 13, 85–88. doi:10.1111/1467-9280.00416

50.

Sloan

(1989). The motives of sports fans. In Goldstein

J. H.

(Ed.), Sports, games, and play: Social and psychological viewpoints (2nd ed., pp. 175–240). Hillsdale, NJ: Erlbaum.

51.

St. Onge

J. R.

Floresco

S. B.

(2008). Dopaminergic modulation of risk-based decision making. Neuropsychopharmacology, 34, 681–697. doi:10.1038/npp.2008.121

52.

U.S. Bureau of Labor Statistics. (2010). 2010 standard occupational classification. Retrieved from http://www.bls.gov/soc/soc_structure_2010.pdf

53.

U.S. Census Bureau. (2015). American Community Survey [Data set]. Retrieved from https://www.census.gov/programs-surveys/acs/data.html

54.

Wright

W. F.

Bower

G. H.

(1992). Mood effects on subjective probability assessment. Organizational Behavior and Human Decision Processes, 52, 276–291. doi:10.1016/0749-5978(92)90039-A

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