=Paper=
{{Paper
|id=Vol-1627/abs1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1627/abs1.pdf
|volume=Vol-1627
}}
==None==
https://ceur-ws.org/Vol-1627/abs1.pdf
Small Differences in Experience Bring Large
Differences in Performance
Sheen S. Levine1 , Charlotte Reypens1,2
1
University of Texas, Dallas, USA
2
University of Antwerp, Belgium
Abstract. In many life situations, people choose sequentially between
repeating a past action in expectation of a familiar outcome (exploita-
tion), or choosing a novel action whose outcome is largely uncertain
(exploration). For instance, in each quarter, a manager can budget ad-
vertising for an existing product, earning a predictable boost in sales.
Or she can spend to develop a completely new product, whose prospects
are more ambiguous. Such decisions are central to economics, psychol-
ogy, business, and innovation; and they have been studied mostly by
modelling in agent-based simulations or examining statistical relation-
ships in archival or survey data. Using experiments across cultures, we
add unique evidence about causality and variations. We find that ex-
ploration is boosted by three past experiences: When decision-makers
fall below top performance; undergo performance stability; or suffer low
overall performance. In contrast, individual-level variables, including risk
and ambiguity preferences, are poor predictors of exploration. The re-
sults provide insights into how decisions are made, substantiating the
micro-foundations of strategy and assisting in balancing exploration with
exploitation.
Keywords: Exploration, Exploitation, Decision Making, Experiment,
Protocol Analysis, Cross-culture
In many life situations – R&D investments, market entry, military campaigns,
romantic choices – a decision-maker chooses an action, receives feedback, and
then chooses again. The choice ranges from repeating a past action in expec-
tation of a familiar outcome (exploitation) to a novel action whose outcome is
largely uncertain (exploration). For instance, in each quarter, a manager can
budget more advertising for an existing product, expecting familiar (but uncer-
tain) sales figures; or he can spend to develop a revised version of the prod-
uct, whose sales prospects are more uncertain; or he can invest in a completely
new product, where prospects are even more uncertain. The optimal action is
not obvious: Probabilities are unknown, feedback is ambiguous. Such decisions
have been discussed across domains and species, from bees and birds foraging
to organizations searching for innovations (for reviews, see [1,2]). We examined
– empirically – how people decide in such situations. To do that, we created
a behavioral task an oil exploration game in which participants earn money
by searching an unfamiliar landscape (cf. [3]). A participant chooses a spot for
drilling and then discovers the quantity of oil it contains. The participant can
keep drilling in the same spot, earning the same quantity of oil. Or he can choose
a nearby location, which likely has a similar oil quantity. Or he can jump across
the landscape to a faraway location, where the oil quantity is likely very dif-
ferent. The participant repeats the choice a fixed number of times. When the
game ends, the oil he found is converted to cash, which is paid to him. The
task faithfully represents the important features of an exploration-exploitation
situation: The landscape is rugged, containing “peaks” and “valleys” of oil, but
there is no map that describes the terrain a decision maker discovers it by
experiencing it. Because probabilities are unknown, optimization is impossible
[4,5,6,7]. And since information accumulates only with experience, initial steps
are necessarily random [8]. The seeker has only limited resources, so he can
sample only a fraction of the entire landscape ([9]). We studied how people de-
cide in exploration-exploitation in four studies. First, we conducted one-on-one
sessions, where we collected quantitative data as well as verbal accounts from
the participants, describing their decision-making process [10]. Second, we con-
ducted laboratory sessions in the U.S. using a web-based version of the game.
Third, also using the web-based instrument, we collected data from workers in
a labor market [11,12]. Fourth, to ascertain the robustness of the findings, we
conducted laboratory sessions in Russia, a country whose history, culture, and
institutions differ from those of the U.S. [13]. Across all studies, we find that
exploration is driven more by immediate experience, less by individual charac-
teristics. Three situations boost it: When a decision-maker falls below his or her
top performance; when he or she experiences performance stability; or when he
or she suffers low overall performance. Exploitation is increased by the reverse
experiences: exceeding top performance, experiencing performance variance, and
enjoying high overall performance. Individual traits, such as risk and ambiguity
preferences, are poor predictors of exploration. These experiences have similar
effects in all of the studies. Behavior is strongly influenced by experience, so two
identical players that face the exact same landscape can undergo completely dif-
ferent experiences and end up with a wide gap in performance, all due to random
differences in their early choices. In everyday life, we often attribute differences
in performance to traits – “she is brilliant,” “this manager is incompetent” –
and not to experience. But the results here suggest otherwise: History matters.
References
1. Hills, T.T., Todd, P.M., Lazer, D., Redish, A.D., Couzin, I.D.: Exploration versus
exploitation in space, mind, and society. Trends in Cognitive Sciences 19(1) (2015)
46–54
2. Mehlhorn, K., Newell, B.R., Todd, P.M., Lee, M.D., Morgan, K., Braithwaite, V.A.,
Hausmann, D., Fiedler, K., Gonzaleza, C.: Unpacking the exploration-exploitation
tradeoff: A synthesis of human and animal literatures. Decision (Washington) 2(3)
(2015) 191–215
3. Mason, W., Watts, D.J.: Collaborative learning in networks. Proceedings of the
National Academy of Sciences 109(3) (2012) 764–769
4. Alchian, A.: Uncertainty, evolution, and economic theory. Journal of Political
Economy 58 (1950)
5. Gittins, J.C., Gittins, J.C.: Bandit processes and dynamic allocation indices. Jour-
nal of the Royal Statistical Society, Series B (1979) 148–177
6. Kauffman, S., Levin, S.: Towards a general theory of adaptive walks on rugged
landscapes. Journal of Theoretical Biology 128(1) (1987) 11 – 45
7. Nagel, R., Vriend, J.N.: An experimental study of adaptive behavior in an
oligopolistic market game. Journal of Evolutionary Economics 9(1) (1999) 27–
65
8. Winter, S.G.: Purpose and progress in the theory of strategy: Comments on gavetti.
Organization Science 23(1) (2012) 288–297
9. March, J.G.: Exploration and exploitation in organizational learning. Organization
Science 2(1) (1991) 71–87
10. Ericsson, K.A., Simon, H.A.: Protocol analysis: Verbal reports as data (Revised
ed.). Cambridge, MA: MIT Press (1993)
11. Horton, J.J., Rand, D.G., Zeckhauser, R.J.: The online laboratory: conducting
experiments in a real labor market. Experimental Economics 14(3) (2011) 399–
425
12. Paolacci, G., Chandler, J., Ipeirotis, P.G.: Running experiments on amazon me-
chanical turk. Judgment and Decision Making (2010) 411–419
13. Henrich, J., Heine, S.J., Norenzayan, A.: Most people are not WEIRD. Nature
466(7302) (July 2010) 29