185 research outputs found
Reciprocity on the hardwood: passing patterns among professional basketball players.
Past theory and research view reciprocal resource sharing as a fundamental building block of human societies. Most studies of reciprocity dynamics have focused on trading among individuals in laboratory settings. But if motivations to engage in these patterns of resource sharing are powerful, then we should observe forms of reciprocity even in highly structured group environments in which reciprocity does not clearly serve individual or group interests. To this end, we investigated whether patterns of reciprocity might emerge among teammates in professional basketball games. Using data from logs of National Basketball Association (NBA) games of the 2008-9 season, we estimated a series of conditional logistic regression models to test the impact of different factors on the probability that a given player would assist another player in scoring a basket. Our analysis found evidence for a direct reciprocity effect in which players who had "received" assists in the past tended to subsequently reciprocate their benefactors. Further, this tendency was time-dependent, with the probability of repayment highest soon after receiving an assist and declining as game time passed. We found no evidence for generalized reciprocity - a tendency to "pay forward" assists - and only very limited evidence for indirect reciprocity - a tendency to reward players who had sent others many assists. These findings highlight the power of reciprocity to shape human behavior, even in a setting characterized by extensive planning, division of labor, quick decision-making, and a focus on inter-group competition
A dynamic over games drives selfish agents to win-win outcomes
Understanding the evolution of human social systems requires flexible
formalisms for the emergence of institutions. Although game theory is normally
used to model interactions individually, larger spaces of games can be helpful
for modeling how interactions change. We introduce a framework for modeling
"institutional evolution," how individuals change the games they are placed in.
We contrast this with the more familiar within-game "behavioral evolution".
Starting from an initial game, agents trace trajectories through game space by
repeatedly navigating to more preferable games until they converge on attractor
games that are preferred to all others. Agents choose between games on the
basis of their "institutional preferences," which define between-game
comparisons in terms of game-level features such as stability, fairness, and
efficiency. Computing institutional change trajectories over the two-player
space, we find that the attractors of self-interested economic agents
over-represent fairness by 100% relative to baseline, even though those agents
are indifferent to fairness. This seems to occur because fairness, as a game
feature, co-occurs with the self-serving features these agents do prefer. We
thus present institutional evolution as a mechanism for encouraging the
spontaneous emergence of cooperation among inherently selfish agents. We then
extend these findings beyond two players, and to two other types of
evolutionary agent: the relative fitness maximizing agent of evolutionary game
theory (who maximizes inequality), and the relative group fitness maximizing
agent of multi-level/group selection theory (who minimizes inequality). This
work provides a flexible, testable formalism for modeling the interdependencies
of behavioral and institutional evolutionary processes.Comment: 4500 words, 4 figures, 1 supplementary figur
Emergence of integrated institutions in a large population of self-governing communities
Most aspects of our lives are governed by large, highly developed
institutions that integrate several governance tasks under one authority
structure. But theorists differ as to the mechanisms that drive the development
of such concentrated governance systems from rudimentary beginnings. Is the
emergence of integrated governance schemes a symptom of consolidation of
authority by small status groups? Or does integration occur because a complex
institution has more potential responses to a complex environment? Here we
examine the emergence of complex governance regimes in 5,000 sovereign,
resource-constrained, self-governing online communities, ranging in scale from
one to thousands of users. Each community begins with no community members and
no governance infrastructure. As communities grow, they are subject to
selection pressures that keep better managed servers better populated. We
identify predictors of community success and test the hypothesis that
governance complexity can enhance community fitness. We find that what predicts
success depends on size: changes in complexity predict increased success with
larger population servers. Specifically, governance rules in a large successful
community are more numerous and broader in scope. They also tend to rely more
on rules that concentrate power in administrators, and on rules that manage bad
behavior and limited server resources. Overall, this work is consistent with
theories that formal integrated governance systems emerge to organize
collective responses to interdependent resource management problems, especially
as factors such as population size exacerbate those problems.Comment: contains supplemen
Composing games into complex institutions
Game theory is used by all behavioral sciences, but its development has long
centered around tools for relatively simple games and toy systems, such as the
economic interpretation of equilibrium outcomes. Our contribution,
compositional game theory, permits another approach of equally general appeal:
the high-level design of large games for expressing complex architectures and
representing real-world institutions faithfully. Compositional game theory,
grounded in the mathematics underlying programming languages, and introduced
here as a general computational framework, increases the parsimony of game
representations with abstraction and modularity, accelerates search and design,
and helps theorists across disciplines express real-world institutional
complexity in well-defined ways. Relative to existing approaches in game
theory, compositional game theory is especially promising for solving game
systems with long-range dependencies, for comparing large numbers of
structurally related games, and for nesting games into the larger logical or
strategic flows typical of real world policy or institutional systems.Comment: ~4000 words, 6 figure
Do We Run How We Say We Run? Formalization and Practice of Governance in OSS Communities
Open Source Software (OSS) communities often resist regulation typical of
traditional organizations. Yet formal governance systems are being increasingly
adopted among communities, particularly through non-profit mentor foundations.
Our study looks at the Apache Software Foundation Incubator program and 208
projects it supports. We assemble a scalable, semantic pipeline to discover and
analyze the governance behavior of projects from their mailing lists. We then
investigate the reception of formal policies among communities, through their
own governance priorities and internalization of the policies. Our findings
indicate that while communities observe formal requirements and policies as
extensively as they are defined, their day-to-day governance focus does not
dwell on topics that see most formal policy-making. Moreover formalization, be
it dedicating governance focus or adopting policy, has limited association with
project sustenance
Machine Translation for Accessible Multi-Language Text Analysis
English is the international standard of social research, but scholars are
increasingly conscious of their responsibility to meet the need for scholarly
insight into communication processes globally. This tension is as true in
computational methods as any other area, with revolutionary advances in the
tools for English language texts leaving most other languages far behind. In
this paper, we aim to leverage those very advances to demonstrate that
multi-language analysis is currently accessible to all computational scholars.
We show that English-trained measures computed after translation to English
have adequate-to-excellent accuracy compared to source-language measures
computed on original texts. We show this for three major analytics -- sentiment
analysis, topic analysis, and word embeddings -- over 16 languages, including
Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing
predictions on original language tweets and their backtranslations: double
translations from their source language to English and back to the source
language. Overall, our results suggest that Google Translate, a simple and
widely accessible tool, is effective in preserving semantic content across
languages and methods. Modern machine translation can thus help computational
scholars make more inclusive and general claims about human communication.Comment: 5000 words, 6 figure
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