The Roots of
Human Sociality:
An
Ethno-Experimental Exploration of the Foundations of Economic Norms in 16
Small-Scale Societies
All
long-enduring political philosophies have recognized human nature to be complex
mixtures of the pursuit of self-interest combined with the capability of
acquiring internal norms of behavior and following enforced rules when
understood and perceived to be legitimate.
Our evolutionary heritage has hardwired us to be boundedly self-seeking
at the same time that we are capable of learning heuristics and norms, such as
reciprocity, that help achieve successful collective
action.
Elinor
Ostrom (1998:2)
In all human
societies, a wide range of social phenomena are governed by self-regulating
institutions, or sets of norms, that prescribe both appropriate behaviors, and
proper sanctions for inappropriate behavior. Such norms influence an enormous range
of human activity, from marriage patterns and sexual inequality to political
processes and market exchange.
However, despite a fair amount of agreement that such institutions exist,
extant theories struggle to explain the genesis and maintenance of the
pro-social norms (e.g., standards of fairness, or rules for punishing norm
violators) that form the bedrock of social interaction (Ensminger and Knight
1997). Both experimental (Bateson
and Shaw 1991, Camerer 2002, Henrich 2000, Henrich et al. 2001, Kagel and Roth 1995,
Kollock 1998, and Rabin 1998) and field data (Fiske 1991, Frank 1988, Kaplan and
Hill 1985, Mueller 1989, Skinner and Slemrod 1985, and Smith 2001) from across
the social sciences indicate that neither assumptions of narrow economic
self-interest, nor evolutionary models based on kinship or reciprocity are
sufficient to account for the observed patterns of human pro-sociality.
To probe the
diversity of social norms and preferences across the human spectrum, in 1998 the
MacArthur Foundation invited twelve experienced field anthropologists to pioneer
the use of experimental economic methods in small-scale societies. Using bargaining and public good
experiments involving real money (for examples, see below under Methods and the
Appendix), our research team has contributed to understanding this puzzle by
showing that 1) people from a diverse range of small-scale societies and ethnic
groups are more altruistic and more willing to engage in costly punishment than
most self-regarding economic and evolutionary models predict, 2) there is
tremendous variation in this pro-sociality across social groups (Henrich et al. 2001 and Smith 2001) and 3)
experimental measurements of pro-sociality are positively related to increasing
market incorporation, socio-economic complexity, and the relative importance of
local cooperative institutions.
In this
second phase of our research, we propose to explore the foundations of social
norms by experimentally measuring individuals’ preferences/tastes for altruism
(or fairness), direct punishment (willingness to punish norm violators), and
third party punishment (willingness of third-party observers to pay a price to
punish unfairness) across 16 small-scale societies. This new work adds both breadth and
depth to our previous findings:
breadth in the form of new experimental data from nine previously
untested small-scale societies, and depth in the form of new experiments and
improved methodological procedures.
Our
interdisciplinary, ethno-experimental approach has a number of advantages over
standard disciplinary projects.
Methodologically, our approach combines rigorous experimental tools from
economics with in-depth, contextually sensitive ethnographic methods from
anthropology. Our international
team of ethno-experimentalists are all experienced field workers, most having
worked in their respective field sites for years. As experts in the local environment and
culture, they are ideally suited to contextualize and interpret their
experimental results. Second,
unlike the situation in standard university-based experiments, our researchers
often have substantial databases on the game participants (that they continually
augment), much of which is based on direct observation rather than
self-report. This intimacy makes
data on variables such as wealth and income more accurate than is typically the
case in experimental work. Third,
with data on actual economic behavior (e.g. patterns of sharing and
reciprocity), we can examine the relationship between game behavior and real
life—a critical but entirely unexplored concern in most experimental
research. Fourth, our use of more
representative samples (all adults and socio-economic categories) has
substantial advantages over the limited samples used in most university-based
work, both for testing a variety of theories, and for generalizing to the
behavior of societies. New data
from non-student populations in the U.S. suggests that students may, in fact, be
quite poor representatives of the larger society (Burks et al. n.d., Ensminger, unpublished, and
Smith 2001). And finally, the
economic context of our populations permits us to use substantial stakes
(typically every game will put one day’s wage on the line) for less than the
cost of standard stakes in industrialized nations. High monetary stakes are important for
assuring that participants focus primarily on the games’ economic incentives,
and not on such things as “what the experimenter thinks” or “what’s more
fun.”
One of the
advantages of assembling such a large and diverse group of researchers is the
variety of theoretical traditions and interests brought to the table. What binds
our group more than anything is a commitment to building an interdisciplinary
science of human behavior, an openness to other theoretical approaches, and a
common respect for methodological rigor.
Among us, we have scholars who have done their major theoretical and
empirical work in the fields of evolutionary theory, psychological anthropology,
rational choice, new institutional economics, development, and network
analysis. Our previous experience
has shown that this theoretical diversity inspires novel ideas, innovative
empirical work, and theoretical development through the challenges that arise
from the clash of paradigms. The
discussion below showcases some of this theoretical diversity and, as was the
case with our previous work, explains how our project can simultaneously
contribute to disparate lines of research.
For organizational purposes we have divided the theoretical section into
two parts: 1) Co-evolutionary Theory examines how our
project will contribute to theoretical work that explores the importance of
social learning, cultural evolution, and culture-gene co-evolution on human
behavior, and 2) Bounded Rationality and
Economic Preferences explores how our work informs various lines of research
that explore how human preferences and networks influence economic
decision-making.
Formal
models in both cultural anthropology and evolutionary economics suggest that
preferences (e.g. altruism, fairness and tastes for punishment), often taken to
be exogenous in economics and innate in evolutionary psychology, may evolve
culturally. By relying on various
forms of social learning to acquire preferences from other members of their
social group, individuals can acquire the appropriate set of preferences and
behaviors for that group. When
aggregated over many individuals and interactions, such learning strategies lead
to the evolution of different kinds of institutions or institutional forms (Boyd
and Richerson 1985, Bowles 2000, and Henrich and Boyd 2001). To the degree that cooperative
institutions afford economic success, the theory predicts that successful
individuals in such institutions will get copied more often. Cooter and Eisenberg (2000) have
similarly argued that fairness promotes efficiency through returns to
cooperation in firms. This provides
incentives for firms to socialize norms of fairness and for people in firms to
change their character to be more fair-minded. Thus, we predict a relationship between
pro-social preferences and experience with, or exposure to, existing
institutions like markets or successful governance
structures.
Cultural
evolutionary models suggest that intra-group processes can lead to a variety of
polymorphic equilibria that may be more or less pro-social—that is, cultural
learning combined with social interaction may produce either cooperative or
uncooperative groups that will resist change (Boyd and Richerson 1990, Henrich
and Boyd 2001). At such stable
equilibria, differences in individuals’ strategies often result, not necessarily
from individual differences such as wealth, education, etc., but from the nature
of the interaction or transmission (e.g. from frequency-dependent payoffs or low
fidelity learning). Which
equilibria a group arrives at is influenced by stochastic, historic, and
ecological factors (including contact with other groups) that make some groups
more likely to land on pro-social (group beneficial) equilibria than other
groups. Such formal models predict
that inter-group differences in pro-sociality may be explained using group-level
variables, while individual-level variables may show no corresponding
relationship. Thus, these theoretical models hypothesize that group-level
ecological and historical variables (e.g. market incorporation) will be able to
account for larger portions of the variation in experimentally measured social
behavior among groups, than the corresponding individual-level variables (e.g.
an individual’s cash cropping). That is, there will be true “group effects”
(multiple stable cultural equilibria).
Our previous empirical work is consistent with this hypothesis (Henrich
et al. 2001), but additional data are
required.
Cognitive
anthropologists and psychologists have argued that evolutionary processes
generated five different ontogenetic learning templates (mental models) that
allow humans to rapidly learn and organize their social relationships without
having to construct new mental models for every individual or social context—or
individual-context interaction (Fiske 1991 and Haslam 1997777). Fiske labels these mental models: Communal Sharing, Equality Matching,
Authority Ranking, Market Pricing and Asocial, although they reappear throughout
the social science literature in various guises. Among particular social groups, cultural
and individual learning connects these templates with both specific individuals
and/or particular contexts. If this
cognitive-evolutionary approach is correct, players should approach our
experimental games by looking for a correspondence between the game
structure/context and one of these mental models. For example, Hoffman et al.
(1994) found that calling the ultimatum game the “buyer seller” game moved
offers toward greater self-interest.
Using
structured interviews (based on instruments adapted from cognitive psychology)
and in-depth understanding of the local culture and institutions, we will
attempt to assess which of the hypothesized mental models is most likely to be
activated by the game context for any given society. These interviews will be conducted with
a separate population of subjects, so as not to contaminate the game
subjects. Depending upon the modal
mental model operative in a given society, we can generate distinct predictions
about players’ behavior in each of our experiments. For example, in Bargaining Games,
proposers using the Equality Matching model should split the stakes 50/50, and
responders would be inclined to reject non-50/50 offers. Yet, if the context activates a players’
Asocial model, proposers should offer very low amounts and responders should
accept any non-zero offer. Our
prior research shows both of these behavioral patterns (depending on the group),
but we lacked the specific cognitive tests to evaluate the data vis-à-vis this theory. Analytically, structural equation
modeling at the group level will allow us to link exogamous institutional and
cultural variables to economic preferences (game behavior) through the
intervening nexus of social cognition (mental models).
In an effort
to address the puzzle of pro-sociality as manifested in economic experiments
(Kagel and Roth 1995), economists are developing new utility theories that can
explain what have appeared to be contradictory patterns of behavior (Bolton and
Ockenfels 1999, Charness and Rabin 2000, Fehr and Schmidt 1998). These theories are rooted in the robust
empirical findings generated by bargaining and public goods games played with
university students. We will
contribute to this line of research in the following ways. By systematically using the same
empirical tools, our research explores the robusticity of these empirical
foundations across the human spectrum.
Do these theories/preferences apply to humans as a species, or are they
applicable only to people in certain places? By allowing us to analyze a fuller range
of human variation, this work will assist us in separating the cultural
evolutionary products of particular local histories from pan-human preferences
(whether these be self-interested—Stigler and Becker 1977, or other
regarding—Fehr and Gachter 2000).
Detailed individual-level data will allow us to test theories that
predict a relationship between game performance and individual-level variables
(e.g. sex, education, age, income, work, and wealth), as well as to explore
intra-individual temporal variation (how much variation is there in one person’s
behavior over time). Studying the
interplay between economic behavior, incentives, and local context will allow us
to illuminate the nature of human preferences: do people have generalized preferences
for things like reciprocity and altruism that operate across all contexts (given
the same payoffs), or do people have situationally specific rules, preferences,
or mental models that operate (are cued) only in some contexts (even when the
payoffs are clearly identical)? Our
previous work has already contributed to this line of research (Ensminger 2000,
Henrich 2000, Henrich et al. 2001, and Henrich et al. n.d.), so the present proposal
aims to replicate and refine our prior work, as well as test some new
predictions.
Another
central form of pro-social behavior is trust. In this project we propose to pursue the
relationship between social networks and social capital in the form of trust and
trustworthiness. Social capital as
a concept in the social sciences has been defined in various ways. One definition, which might be termed
“social capital as opportunity,” views the amount of social capital an
individual actor has as the degree to which he or she has the ability to bring
together, or bridge, a wide range of other actors who are themselves not
connected (Burt 1992, 1997, Johnson et
al. 2001, Lin 1999, 2000a, 2000b, 2001 and Lin et al. 2001). Such a structural position may allow an
individual to influence the flow of information and knowledge that leads to
economic success and political advantage.
In this case, actors with high social capital have low density and
non-redundant network ties (an actor's contacts are themselves not connected to
one another). This allows actors
the freedom to exploit structural gaps or holes in the network (Burt 1992). This does not necessarily predict more
self-interested behavior, as it may be impossible to maintain such a position if
one abuses it. It may predict
strategic talent, however, as such individuals need to have mental acuity to get
where they are and to stay there.
Whatever their preferences, we may expect them to be more adept at
calculation.
Another
general use, which we will label “social capital as security,” sees social
capital as the degree to which an actor is embedded in a dense set of social
relations--the denser an actor's relations, the higher their social capital
(Coleman 1990, Portes and Sensenbrenner 1993). This use stands in stark contrast to the
previous definition. Here density
provides security for individual group members in that it protects them from
potentially negative outside influences (e.g., out-group conflicts) and promotes
a more certain social environment (i.e., social norms are clear). In addition to protection, such dense,
cohesive networks foster cooperation and provide members with a sense of
belonging and identity (Portes and Sensenbrenner 1993)—that is, they imbue
actors will pro-social preferences.
Some see this form of social capital, at both the individual and group
level, as both reflecting and creating the degree of trust in a given society
(Putnam 1993, 1996, 2000). But this
form of social capital comes with a cost.
Such dense, redundant social relations often entail various social
obligations and restrictive norms (Portes and Landolt 1996).
These two
definitions of social capital raise interesting behavioral questions. Under social capital as opportunity, if
we hypothesize that “middlepersons” hold their positions largely due to their
strategic skills, we should expect them to have better predictions about how
others play the games. If they are
also opportunists, we should expect them to make low offers in the dictator
game. Alternatively, if they hold
their positions largely because they are trustworthy, we should expect this to
be evident in their behavior in the trust game as Player B’s. Actors with high social capital as
security might be expected to be more trusting in their approach (as reflected
in higher offers in the trust game and in public goods games). Similarly, societies with high social
density should exhibit higher levels of trust overall. These two approaches also generate
predictions about the variance we will find in intra-society distributions. In the sense of social capital as
security, we expect to find greater consistency in offers around a single mode
when social density is high and there are more normative constraints, but less
consistency when it is low.
Similarly, individuals who fill structural holes in the first definition
may not be bound by local norms in the same way that others in a society are,
thus we may find their behavior off the mode in a variety of games. One question we are particularly
interested in asking in this research is whether there is any relationship
between a person’s position in the network and pro-social punishment
behavior? For example, are those
filling structural holes the sorts of individuals who can and are expected, to
exert sanctioning behavior against others?
Finally, the
data collected in this project will allow us to weigh in on an increasingly
prominent debate in development circles.
In a new book, Platteau (2000) builds upon the work of early
modernization theorists such as Foster (1965), and argues that equity norms
found in many small-scale societies in Africa and Latin America are the
explanation for failed development.
His reasoning is that redistribution in such societies suppresses
individual incentives to produce because there is an implicit community “tax” on
all surplus production. This is of
course an old idea from anthropology, but it is undergoing a recent resurgence
among development scholars. The
data from our first round of experiments stand in stark contrast to this
perspective, as it is the small-scale, low market-oriented societies that make
the least equitable divisions in our bargaining experiments. In contrast, our highest levels of
equitable behavior are found in the more market oriented and “developed”
societies. Further replication of
our earlier work will help us conclusively weigh in on this
debate.
Prior
Experimental Results of our Research TEAm
Building on
the limited cross-cultural work previously done among university students in
Israel, the Slovak Republic, Japan, Indonesia, and Hong Kong (Cameron 1999,
Kachelmeier and Shehata 1997, Roth et
al. 1991, and Yamagishi 1994), our original research team performed a
mixture of Public Goods, Dictator, Ultimatum, and Trust Games in 15 small-scale
societies (see brief game descriptions later in the text and detailed protocols
in the Appendix). Most of these
studies are compiled in Henrich et
al. (n.d.), and synthesized in Henrich et al. (2001), although new field
studies continue coming in. The
most substantial comparative findings from our project can be summarized in six
points. First, while our earlier
results are similar to previous experimental findings in some respects, these
results show much greater cross-cultural variability than prior research had
indicated. Second, these results
broaden and bolster the findings of previous experimental work in testing models
of narrow economic self-interest.
Such self-regarding models not only fail to explain the data from these
15 societies (as they had among university students), but they fail in new ways,
and to varying degrees, in different places. Third, group-level differences in market
integration and the importance of cooperative institutions can explain a
substantial portion of the behavioral variation among societies. Our strongest linear regression finding
shows market involvement and economic organization can account for more than 60%
of the variation in pro-social behavior (both fairness and punishment) across
the 15 societies. Fourth, unlike
group-level variables, individual-level economic and demographic variables (age,
sex, education, income, and household wealth) do not consistently explain any
portion of the variation across the entire dataset, or within individual
groups. Fifth, although group-level
differences seem to account for much of the variation, quite different
behavioral patterns do arise in populations living side-by-side or intermixing
in the same environment. Sixth,
experimentally observed behavioral patterns seem to reflect the economic life
and history of the people from which they arise. People do seem to bring their real
lives, their institutions, and their culture into one-shot games (Hoffman et
al. 1996).
Using the
experience gained during our first project, we will build on and attempt to
replicate our previous findings with both new experiments and improved
protocols; we will also gather more rigorous measures of individual and
group-level measures (such as market incorporation). Below, we begin by describing the
demographic survey and the “Core Package” of experiments that will be performed
in all 16 societies. This package
is intended to provide comparative measures of altruism, direct punishment, and
third-party punishment. In this
section, we detail the individual level variables (sex, age, education, work,
income, and wealth) and group-level measures (level of market integration and
contributions to cooperative labor) that will be collected for analysis with the
experimental data. We then detail
four sets of supplemental experiments that will be performed among selected
subsets of our total sample of societies.
These supplemental experiments address a variety of theoretical and
methodological issues, including an examination of trust and social networks,
contextual or framing effects, experimenter bias, and intra-individual temporal
variability. Following this, we
briefly describe the sample of societies to be studied. Finally, we explain our research goals
and sketch some of our analyses and plans for the dissemination of our results.
We propose a
three-game experimental package for use in all 16 societies. This Core Package is designed to measure
altruism, direct punishment, and third-party punishment using these games: 1) the Dictator Game (DG), 2) the
Strategy Method Ultimatum Game (SMUG), and 3) the Punishment Game (PG). Below we provide a basic description of
each experiment and the kind of data it generates (the Appendix provides
detailed protocols).
The three
games described below will all be administered in a transparent fashion. All players receive the same
description; all money is real and equal to one day’s wage in the local economy;
no deception is involved; all games are one-shot. All players are anonymous to one
another, in the sense that they do not know specifically with whom they are
matched, although they will know that they are matched with a person or persons
from the experimental group or local community. It will be obvious from the game setup
that the experimenters will know players’ decisions (although this will not be
explicitly stated). Our
supplemental experiments, which are discussed in a later section, will
incorporate “double blind” procedures (where even the experimenter does not know
how specific individuals played), which are designed to test for any effect of
the experimenters’ knowledge of individual decisions.
Core
Experiment 1--Dictator Game (DG)
Procedure: Players A and B are allocated a sum of
money. Player A decides how this
sum is divided. He or she
“dictates” how much money B receives and retains the
remainder.
Intended
Measurement and Analytical Interpretation: This measures pure altruism, or some
notion of fairness. Purely
self-interested individuals should keep all the money for
themselves.
Core
Experiment 2--Strategy Method Ultimatum Game (SMUG)
Procedure: Players A and B are allotted a sum of
money. Player A must decide how to
divide this sum between his or herself and Player B. Player B, before hearing the offer, must
set an “acceptable offer range.” If Player A’s offer falls within this range,
Player B receives the offer, and Player A gets the remainder. If A’s offer is outside this range,
neither player receives anything (the money disappears). In most societies, this offer range will
take the form of a minimum acceptable offer (Knez and Camerer 1995), although
research from two competitive gift-giving groups in Papua New Guinea (Tracer
n.d.) indicates that some people will reject offers that are too
large.
Intended
Measurement and Analytical Interpretation: The SMUG provides two measurements. First, when Player B’s behavior comes in
the form of minimum acceptable offers, it can usually be interpreted as
measuring how willing individuals are to punish others’ unfairness at a cost to
themselves. We call this direct punishment. Post-game interviews and focus groups
(described later) will assist in assessing whether a particular SMUG is
measuring direct punishment. When
the acceptable offer range comes in other forms, such as accepting “offers
between 30% and 60%,” understanding what the two boundary points measure will
depend on both post-game investigations and the field worker’s ethnographic
knowledge. The lower bound, for
example, may represent a measure of direct punishment, while the upper bound may
show a fear of indebtedness (Tracer n.d.).
Second, Player A’s offer measures a combination of preferences for
fairness (or altruism) and fear of direct punishment. By combining these offer data with A’s
beliefs about what B’s will do (see later), and/or the actual behavior of B’s,
indirect measures of altruism (or notions of fairness) can be derived. Further, these data can be used to
calculate how accurate A’s beliefs are about B’s behavior.
Analysis of data from ultimatum (SMUG)
and dictator game (DG) experiments:
Subtracting DG offers from UG offers also yields the effect of direct
punishment on offers, and partitions “fair behavior” into altruistic and
punishment-induced components.
Indirect measures obtained from the SMUG data alone and the direct
measure provided by the DG yield comparative measures of altruism. Differences between these two measures
may suggest a variety of explanations.
For example, A’s may have the wrong beliefs about what B’s will do (which
the SMUG may corroborate), or the DG and UG may cue different culturally
acquired models of behavior that produce entirely unrelated sets of data, as
Smith has suggested (2001). In
other words, the DG could cue fairness (e.g. Equality Matching mental model),
while the UG could cue strategic behavior (Market Pricing mental model)—make the
lowest offer you can get away with.
Core
Experiment 3--Punishment Game (PG)
Procedure: Players A
and B are allotted a sum of money (e.g., $10). Player A must determine the amount of
money B receives, as in the dictator game (DG). Player C receives half the sum allotted
to A and B (e.g., $5) and also learns of Player A’s division. C has the opportunity to pay from zero
to $5 to inflict punishment on A at 3 times his or her (C’s) cost. So, if A gives only $1 to B, C might pay
$2 to punish A, which subtracts $6 from A’s $9. In the end, A leaves with $3, B with $1,
and C with $3 (Fischbacker and Fehr n.d.).
Intended
Measurement and Analytical Interpretation:
Player C’s behavior in the punishment game measures an individual’s
willingness to inflict costly punishment on someone who has behaved “unfairly”
in an exchange that does not directly affect the punisher—i.e., it measures
their taste for third party punishment.
Further, using Player A’s behavior in combination with the dictator game
data, we can derive measures of how much the possibility of third party
punishment affects A’s behavior.
With enough data on C’s behavior in the punishment game, we will be able
to calculate what an “income maximizing” A would do (income maximizing
individuals attempt to make the most money given the actual likelihood of
punishment in the population), and compare this with A’s actual behavior. Subtracting an income maximizing A’s
behavior from A’s actual behavior yields an indirect measure of altruism, which
can be compared to our prior measures of altruism. Eliciting A’s beliefs about what C will
likely do will allow us to compare A’s beliefs with C’s actual behavior, and
assess the degree to which A’s decisions result from mistaken beliefs about
C.
Prior to
running the actual experiments, we will collect the more extensive
socio-economic data on individuals and households. Because many of these data are time
consuming and sensitive to collect (especially individual income and household
wealth), this can best be done in a more leisurely fashion than is afforded in
most experimental settings. For
researchers surveying entire villages, such surveys also furnish censuses from
which representative samples can be drawn for the experiments. Income and wealth surveys will be
tailored to the local resource base, with an effort to quantify all sources of
productive capital in the wealth measures.
Two measures will receive special attention, as they emerged as key
variables in our last round of experiments: market integration and cooperative
institutions.
Market
Integration: Our previous work has suggested that
group-level measures of market integration and the local importance of
cooperation may be potent predictors of group-aggregated experimental behavior
(Henrich et al. 2001). However, because our previous
group-level measures were rather crude, we will endeavor to improve these. Market integration will be measured by a
variety of methods to test for robustness.
One measure will be the amount that each individual receives from wage
labor, trade, cash cropping, or through any other contact with the market. A second measure will be based upon the
percentage of daily food consumption that is purchased in the market. In the extreme case, pure subsistence
producers living one hundred percent off their crops, herds, or hunting, will be
recorded as having zero market integration. In most cases we will gather individual
level measures of market integration, but where time does not permit, a random
sample of people will be surveyed to create group-wide averages of these
variables for cross-cultural comparisons.
Cooperative
Institutions: Local cooperation will be measured using
data on time spent in cooperative endeavors. We will gather individual level data on
days per week spent in cooperative activities with non-close-kin (cooperative
tasks involving kin more distant than half-sibs, both classificatory or by
blood). Such tasks will include
community projects such as water projects, canal cleaning, school maintenance,
well digging, herding, forest clearing, work parties for weeding or harvesting,
etc. Obviously, each ethnographer
will have to adapt methods of acquiring this measure (e.g., in many societies
this will vary by season).
Network
Measures: Those who are participating in the
sub-group analyzing the relationship between social networks, trust, and other
strategic behavior, will attempt to capture entire village networks in an effort
to create measures of network density and centrality. Such complete network analyses will also
facilitate the identification of individuals occupying “structural holes” in the
network. Prior to the date for the
experiments, a complete network analysis of the village will be carried out with
all adults who are eligible to play the games (adults over the age of 18). Three questions will be asked: “Tell me all the people with whom you
interact most on a day to day basis.”
“Tell me all of the people from whom you could borrow the equivalent of
one day’s minimum wage with no collateral.” “Tell me all of the people to whom you
would be willing to lend one day’s minimum wage with no
collateral.”
Below we
describe our target experimental protocol.
To fit these experiments to local situations, field researchers may need
to modify the details. However,
every effort will be made to minimize those modifications, and, whenever
possible, to test (or control for) the effects of any modifications. All deviations will be recorded and
entered into the master database.
We learned a
great deal from our first round of experiments concerning protocols and methods
that will both maximize controls across sites and minimize the possibility of
local “contagion effects.” The
latter is potentially a considerable problem in small communities where almost
everyone knows one another. Games
will be run with individuals who have not previously played any game, and
preferably in villages where there has been no prior exposure to the games. Each game in the Core Package will be
run on three successive days to minimize “talk” before the core experiments are
finished. No individual will play
more than one core game to avoid any learning effects that might run across
games.
Our goal is
to obtain a sample size of at least 30 pairs/trios for each game, meaning we
need 60 people (A’s and B’s) for the ultimatum and dictator games, and 90 for
the third party punishment games (A’s, B’s and C’s). Individuals will be randomly drawn from
a census of all adults in the village over the age of 18. On a given morning, 30 people will be
called for the dictator, the ultimatum, or the punishment game. These people will be gathered together
in a public area—a school or a shade tree, and monitored by research assistants
to prevent talking about the game, though at this point no one should have any
notion of the specific rules. At
this time, subjects will be reminded that all participation in the games is
purely voluntary, and they are free to leave at any time. They will also be reminded that the
games may take up to 3 hours and will involve completion of a short survey while
they wait to play. During the
waiting period, any demographic information that has not already been gathered
will be collected: sex, age,
education, income, work history, and household wealth. If data on market integration and
cooperative work have not already been collected, these will be included. Research assistants will gather these
data person-by-person.
One by one,
people will be called in to play the game, which will be explained to them in a
private area. Prior to actually
playing the games, participants must correctly answer a set of hypothetical test
questions. For example, in the
ultimatum game, the researcher might ask something like, “Suppose A offers $2
(of $10) to B, and B set his minimum threshold at $3. Does B know A’s offer when he decides
his threshold? [Answer: no]. Does A know B’s minimum amount when he
decides his offer? [Answer: no]. How much money will A receive? [Answer: 0]. How much money will B receive? [Answer: 0]. What if B had set his threshold at
$2? How much money would A
receive? [Answer: $8]. How much money would B receive? [Answer: $2].” This testing process will be repeated
until the respondent gives at least three sets of correct answers in a row. Game descriptions are available in the
Appendix. After playing,
participants will leave the experimental area, but will not be allowed to
interact with participants still waiting to play. At the beginning of the game, it will be
made clear that any discussion between waiting participants and those who have
completed the game will cause the waiting individual to forfeit their
opportunity to play. Those who are
assigned the role of player A in the ultimatum game or the punishment game will
be assigned a specified time to return, in order to find out if their offer was
accepted.
This
procedure will be repeated the same afternoon with the final set of 30 players
for experiments 1 and 2. For the
punishment game, an extra group of 30 people will be called to serve in the
Player C role of punisher. Pay-offs
will be scheduled to follow all of this play. All payments will be
private.
Post-game
interviews. After each player has completed the
game, he or she will be asked, “What would this game make most people
around here think of?” and, “Name
this game: what is a good name for
this game?” These questions will provide insight into how people perceive the
games within their own local context, and how they might be “mapping” them onto
common situations, mental models of social relations, or local
institutions. Players will also be
asked, “What would most people do” as Player A, B, and C (if applicable). Eliciting beliefs is critical for
testing rational choice models that predict individuals will maximize utility
based on their beliefs (accurate or otherwise) about what most people will
do. These questions will form a
foundation for the in-depth ethnographic inquires that will follow the
experiments.
Once all of
the games are completed, small focus groups will be set up to discuss the
“meaning of the games,” the local interpretation of the results, what people are
saying about the game (the “buzz”), what they think of its outcomes, and whether
they would behave differently in the future. Focus groups will be composed to
encourage participation, thus sex and large status differences will be avoided
(where appropriate). These questions will provide valuable ethnographic insight
into how people interpret the experiments, and what such games are actually
measuring.
In order to
better connect our findings with the vast number of studies from industrialized
societies and the literatures of experimental economics, economic sociology, and
cognitive psychology, we intend to replicate all of our Core Package and
experimental protocols with three U.S. populations—among non-students in rural
Missouri and in the city of St. Louis, and among U.S. university students in
Atlanta. One of the variables that
we consider crucial to reproduce is the “small community” context that is
consistent across all of our sites, but not typically found in laboratory
studies. In Missouri, the small
rural town is roughly comparable in size to some of the communities from our
survey areas around the world. In
St. Louis city the games will be run with people from neighborhood groups and
organizations. Funding for the
Missouri studies has already been procured by the co-PI (Jean Ensminger) from
the Russell Sage Foundation and this work began in summer 2001. We will also run the Core Package and
some supplemental experiments in Henrich’s experimental laboratory at Emory
University, with students from the same campus organizations. These controls will allow us to connect
our field results to the populations typically used by
non-anthropologists.
Supplemental
Experiments
In addition
to the core experiments described above, which will be performed by all 16
researchers, sub-sets of our group will also coordinate to perform four sets of
supplemental experiments. These
supplemental experiments will 1) address theoretical questions dealing with
trust and social networks, 2) examine framing effects created by manipulating
the presentation of the games vis-à-vis the local culture, 3) estimate
the effect of the experimenter’s knowledge of subjects’ decisions, and 4) assess
the temporal, intra-individual replicability of game behavior (i.e. what happens
if the same games are repeated with the same subjects).
The core
experiments focus on measuring altruism and punishment, but there are several
other dimensions to pro-sociality.
To measure both trust and trustworthiness, Barr, Barrett, Ensminger,
Henrich, Gwako, Johnson, Lesorogol, and Patton will use the trust game developed
by Berg, Dickhaut, and McCabe (1995 and see Appendix) in conjunction with
network analysis. In this game,
both Player A and Player B are given an equal endowment. Player A is then given the opportunity
to send any portion of his or her endowment to Player B, with the understanding
that whatever is sent will be tripled by the experimenter and that Player B will
then have the option of returning any portion of it to Player A. Results from this game will provide
measures of both trust (Player A’s behavior) and trustworthiness (Player B’s
behavior). As described above,
these experiments will be especially interesting to examine in the context of
data on social networks. In the
post-game debriefing we will ask what the game reminds people of, and what name
they would use to label the game.
This contextualization should verify whether people conceptualize the
game as a “trust” situation.
Our prior
research suggests that game performance may depend on participants making
cognitive links between the games and existing institutions or practices
(Pillutla and Chen 1999). Among the
Orma (Ensminger 2000, n.d.), for example, players seemed to recognize some
similarity between the Public Goods Game and the existing institutional form of
fund raising for community development known as harambee. This may have contributed to
the Orma’s high proportion of full-cooperators (25%), and generally high mean
contributions (58%). People may
look for analogies between these abstract games and real life institutions. If the game appears sufficiently
similar, individuals may use the corresponding institutional rules as anchors,
or defaults, in their decision-making.
Given this
possibility, we need to take control of the “context” and examine how
manipulating the context vis-à-vis
local cultural models, norms, and institutions affects behavior. Among Fijians, Tsimane, and Sursurunga,
we propose to administer two versions of the SMUG and Public Goods Game. Following the general procedure laid out
above, participants will enter the game area and face the same game (i.e., the
same payoff structure) presented in two different contexts or scenarios. In one scenario, the game will be
dressed-up to cue a particular institution (a set of shared behavioral rules)
that prescribes pro-social behavior (equity or cooperation) in a social
interaction. The other scenario
will be framed either in the usual abstract context, or dressed up to resemble a
local context that lacks pro-social norms.
For example,
among the Yasewans of Fiji, both the SMUG and PGG can be framed merely by
replacing the cash with Kava roots.
Fijians explicitly distinguish (and recognize) two ways of thinking about
exchange, “the way according to kinship” (the gift economy), and the “the way
according to money” (the market economy).
Although most Fijians participate in both spheres, they get very upset
when these two spheres of exchange are confused or become intertwined. In most contexts within a village,
friends and fellow villagers don’t set prices, wages, or work standards. Conversely, in business contexts, family
members and friends should not expect a better deal from their store-owning
relatives (Toren 1999). In the gift
economy, individuals are ranked by the status of their lineage and their own
prestige, so gift exchange reflects an inherent inequity that pervades village
life. Nevertheless, all gifts bring
the giver prestige, and the more generous the gift the more prestige for the
giver. In the market sphere,
individuals are equals, with each trying to get the best deal for him or
herself. These two ways of
organizing exchange are symbolized by the two quintessential mediums of
exchange: cash and Kava roots. Kava roots are used to make a mildly
intoxicating (non-alcoholic) beverage that is the foundation for most social
gathering in Fiji. People grow Kava
in order to give it way, either in its root form or during social occasions as a
communal drink. Interestingly,
because of its tremendous importance in Fijian society, Kava can readily be
converted to cash in the market place, and virtually everyone knows the
price. Nevertheless, merely by
switching from cash to the Kava-cash-equivalent, participants will likely be
“cued” into deploying the cultural rules or norms associated with the gift
exchange sphere. As compared to the
cash version: Player B’s in the
SMUG should set lower acceptable thresholds (with zero as the mode) because the
cultural rules of the gift-sphere specify that gifts must not be demanded using
retributive threats; Player A’s in both the SMUG and DG will tend to offer/give
more, even yielding hyper-fair offers (offers > 50%), because the rules of
gift-sphere specify generosity, and even great generosity (especially with Kava,
which is produced solely for ‘giving’ or sharing). In the public goods game, players using
Kava should make substantially higher contributions to the group, as compared
with the cash version, with the mode at full cooperation (giving it all to the
group). Games presented in both contexts will be followed up by extensive
post-game interviews aimed at understanding precisely how individuals understood
the games, and how they thought other members of the group understood the
game. We will also identify the
cognitive Fiske model used, and include a series of “matching games” that
provide people with financial incentives for accurately guessing what other
people said about the game (e.g., what most people named
it).
Because our
experiments are typically administered by individuals who may have more personal
relationships with locals than experimenters in universities, the influence of
experimenter bias must be examined.
Double blind experiments have been developed in which subjects are
assured that the experimenter will not know exactly how they played as
individuals, although the experimenter will know the distribution of the
data. Initial efforts using
double-blind setups with the Mapuche of Chile and the Samburu of Kenya
(Lesorogol’s work) suggest that experimenter effects are small or
non-existent. With the Sangu, Orma,
Samburu, Maragoli, and Garifuna, we will perform both double- and single-blind
dictator and one-shot Public Goods Games.
Following our protocol above, participants will be gathered together and
randomly assigned to treatment conditions in a transparent fashion (e.g. picking
chips from a hat). See Bolton et al. (1998) and Hoffman et al. (1994) for double-blind games
among university students; see Eckel and Grossman (1998) for gender effects in
double-blind games.
No research
that we know of has explored the temporal variability of individual behavior
over long time periods. Will
individuals change their game behavior if the game is played again, separated by
months, or years? How much
variability is there in one person’s behavior? In other words, are behaviors such as
altruism in the dictator game characteristics of certain “types of people,” or
are they situationally driven by specific individual circumstances? To explore this, Ensminger, Gurven,
Lesorogol, and Tracer will repeat experiments with the same individuals, with
plays separated by months or years.
After their second play (but not on any subsequent plays), players will
be asked to recall what they did on their first play and what happened. If their current play varies, they will
be asked to explain why they have changed their preference.
To explore
the variability in social behavior across the human species, we selected a
diverse array of 16 societies from across the globe (Table 1). Coming from 11 countries scattered
across Asia, Africa, New Guinea, the Americas, and Oceania, our sample (double
counting for mixed strategies) includes 5 groups of foragers, 6 groups of
swidden horticulturalists, 4 groups of pastoralists, 3 groups of small-scale
farmers, and 3 groups of market-based producers/wage laborers. Our foragers span a wide range of
socio-political complexity and market integration, and show a diversity of
cooperative institutions. In
Tanzania, the Hadza’s acephalous bands rely almost entirely on foraged foods
(tubers, berries and game) and share large game widely. On isolated parts of Columbia’s
Caribbean coast, the Garifuna economy is based on fishing and gathering
resources, many of which are exchanged for market goods. In the Russian Arctic, the Inupiaq
combine their hunting and fishing with wage labor. In the Torricelli mountains of Papua New
Guinea, the village-based Au and Gnau combine hunting and horticulture with a
heavy reliance on foraging for sago palms, and exchange very little in the
market. As in many Melanesian
communities, redistribution gives prestige, and putting others in debt is a
political tool used to gain status.
Requests for goods must be granted, and fear of being indebted is a
constant concern. Relying more
heavily on swidden horticulture, the Quichua, Achuar, and Shuar of the
Ecuadorian Amazon, the Sursurunga of New Ireland (Papua New Guinea), the Tsimane
of the Bolivian Amazon, and the Yawasans of Fiji, all rely primarily on garden
produce, and specifically on root crops (manioc, taro, and sweet potatoes) and
bananas, which they supplement with hunting, gathering, and/or fishing. Despite these economic similarities, the
organization of labor in these communities differs substantially. Both the Tsimane and the Ecuadorian
groups (Shuar, Achuar, and Quichua) rely almost exclusively on household labor,
but the Ecuadorian groups also exploit a community-wide cooperative institution
(Mingas). Meanwhile, in the South Pacific, the
Sursurunga and the Yawasans rely more heavily on community-based production that
is regulated by rigid hierarchies.
Our pastoral populations—the Orma of Kenya, the Sangu of Tanzania, the
Samburu of Kenya, and the Torguud of Mongolia—also show high variation, both
within each society and across societies, in the degree to which they rely on
subsistence production versus sale of stock. The small-scale farmers in our sample
include the relatively poorer and less educated Sangu of Tanzania who practice
extensive farming, and the highly concentrated and commercialized Maragoli of
Kenya, with tiny farms, enormous population pressure, and high levels of
education. In the infertile
mountainous soils of north-central Mexico, the semi-nomadic Huichol live in
widely scattered family hamlets, practicing swidden agriculture and animal
husbandry. Many Huichol men migrate
in the off-season in search of wage labor.
TABLE
1
Researcher |
Group |
Country |
Economic
Base |
Experiments |
Barr |
Multi-ethnic |
Ghana |
Wage
Labor |
Core,
TG |
Barrett |
Shuar |
Ecuador |
Horticulture-Foraging |
Core,
TG |
Bolyanatz |
Sursurunga |
New
Ireland |
Horticulture |
Core,
CTX |
Cardenas |
Garifuna |
Columbia |
Fishing-Market
Prod. |
Core,
DBG |
de la
Pena |
Huichol |
Mexico |
Farming |
Core |
Ensminger |
Orma |
Kenya |
Herding |
Core,
TG, TSR, DBG |
Gil-White |
Torguud
& Kazaxs |
Mongolia |
Herding |
Core |
Gurven |
Tsimane |
Bolivia |
Horticulture-Foraging |
Core,
CTX, TSR |
Gwako |
Maragoli |
Kenya |
Farming |
Core,
TG, DBG |
Henrich |
Yawasa |
Fiji |
Horticulture |
Core,
CTX, TG |
Johnson |
Inupiaq |
Russia |
Foraging
& Wage Lab. |
Core,
TG |
Marlowe |
Hadza |
Tanzania |
Foraging |
Core |
McElreath |
Sangu |
Tanzania |
Herding
and Farming |
Core,
DBG |
Lesorogol |
Samburu |
Kenya |
Herding |
Core,
TG, TSR, DBG |
Patton |
Achuar
& Quichua |
Ecuador |
Horticulture |
Core,
TG |
Tracer |
Au
& Gnau |
Papua
New Guinea |
Horticulture-Foraging |
Core,
TSR |
RESEARCH
GOALS AND QUESTIONS
The overall
objectives of this research are to replicate our earlier work, broaden the
research by including new sites and new experiments, tighten the data collection
methods across sites, and extend the research with new predictions as outlined
above. Below we suggest related
questions that we will address.
Question
Addressed by Core Experiments
1.
How much
variation is there in tastes for altruism, direct punishment, and third-party
punishment? What is the variation
within groups and between groups?
2.
Can any of
this variation be explained by individual-level economic or demographic
variables? Why is there more
individual variation in some groups than in others (Henrich et al. n.d.)?
3.
Data suggest
(Ensminger n.d. and Henrich et al.
n.d.) that market integration may contribute to explaining equity-preferring
behavior both in bargaining games and in the real world. Is this result replicable with new
societies and do we find evidence of both inter- and intra-societal variation in
offers by degree of market integration?
4.
Can the
group-level variation be explained by the presence of local cooperative
institutions, markets, socio-political power structures or other group-level
variables? Are these group-level
effects merely products of aggregated individual variation, or are they true
group effects? As mentioned in the
theory section, multi-level, cultural evolutionary game theory predicts true
group effects, while most rational choice approaches do
not.
5.
How much of
the apparent pro-social fairness in bargaining games results from direct
punishment or third-party punishment?
How much is pure altruism?
6.
How accurate
are people’s expressed beliefs about what others will do in the game, and do
these beliefs influence game decisions?
How much variation is there within, and across groups, in beliefs about
what other group members will do in the game. What individual and group level
variables predict belief accuracy?
Cognitive approaches predict that subjects who relate the games to
similar real world contexts (e.g., institutions, mental models) will show high
degrees of correspondence (high predictive accuracy).
7.
How much
variation is there in the accuracy of the experiments as measuring devices for
these preferences? Do the DG and
SMUG measures of altruism correspond?
Questions Opened by Supplemental
Experiments
1.
How much do
trust and trustworthiness vary among individuals and groups? Can individual-level variables, like
placement in social networks, explain these differences? Does the level of socio-political
complexity, market integration, or network density, predict levels of trust and
trustworthiness?
2.
Do the trust
and trustworthiness of groups co-vary?
That is, do player B’s tend to respond to player A’s trust with
trustworthiness?
3.
Are
individuals’ beliefs about the trust and trustworthiness of other members of
their group accurate? Do any of our
individual variables (e.g. network centrality, market participation) or group
variables (e.g. network density, market integration) predict this accuracy
measure?
4.
How
important is the contextual interpretation of the game by the subject? Does connecting the games with
local institutional forms or shared cultural models have consequences for
individual performance? How strong
are these effects? Cognitive approaches (Fiske 1991 and Shore 1996) predict they
should be extremely important, while most rational choice models predict little
or no effect.
5.
Do decisions
in experiments result from dispositional differences among individuals and
groups or from differences in contextually cued rules about how to behave in
specific circumstances that vary from group to group, and from situation to
situation? That is, are some
individuals or groups generally more fair-minded or pro-social than others,
across contexts? Or, do individuals
and groups move relative to one another as contexts
change?
6.
How much
intra-individual temporal variation is there? Does this vary among groups? Can either individual or group-level
variables account for any of the variation in intra-individual temporal
variation? Do people’s explanations
of the variance point to a larger roll for “situational” circumstances such as
dire economic need, rather than player “type” (self-interested versus
other-regarding) in explaining offers?
7.
How much
does the experimenters’ knowledge of players’ behavior influence players’
behavior?
Post field
work Analysis and Dissemination
After all
researchers return from the field, we will hold a small conference attended by
our researchers and a distinguished collection of senior social scientists. Each researcher will be required to
deliver and present a completed paper summarizing his or her findings. In these papers, each researcher will
perform a number of standard calculations and analyses on their basic Core Data
Package. These will include a
series of regression analyses of A, B, and C’s behavior (where appropriate)
using economic and demographic variables that everyone collected (age, sex,
education, income, work, market integration, and household wealth) and
standardized graphical presentations of the data for easy comparisons. Beyond this, researchers will customize
their analysis and discussion, depending on their sites and particular
theoretical interests. In addition,
each researcher will deliver an Excel data file containing his or her
experimental data (for the Core Package), as well as the standard
individual-level data used in their regressions. Researchers will be supplied with a
formatted Excel file prior to leaving for the field to ensure uniform data
collection. Variations from the standard protocol will also be meticulously
recorded for each site and game and coded in the master database to check for
effects. The P.I.s will also
analyze the entire dataset of behavioral measures using the same
individual-level demographic variables and the group-level measures of market
integration, cooperative institutions, and socio-economic complexity. The P.I.s will look for methodological
explanations for cross-group effects, such as the effect of game and village
order effects that could lead to pollution of populations, effects associated
with deviations from the set protocol, etc. Once compiled, the master dataset will
form the foundation of at least one summary paper intended for journal
publication. The project papers
will also form the foundation of at least one edited volume. Everything will be made available on our
project website. A second
conference will be scheduled approximately one year after our first one. At this conference, we will discuss new
empirical and analytical findings, lessons, and new directions for our third
round. Caltech has agreed to donate
the services of one full-time graduate student to assist in the data management,
analysis, and conference coordination for all three years of the
grant.
As our
research team has nearly completed this process once, interested readers are
directed to the website (see
webuser.bus.umich.edu/henrich/gameproject.htm). Our previous work is forthcoming in an
edited volume (Henrich et al. n.d.), and summaries of our first
project have been, or will be, presented at the American Economic Association
(2001 and 2002), Human Behavior and Evolution Society (1999), American
Anthropological Association (1999 and 2001), and the International Society for
the Study of New Institutional Economics (1999 and 2001). Furthermore, popular summaries of our
findings have been reported in New
Scientist, Facts and Nature
magazines, on BBC radio, and in German, English and French
newspapers.
Inquiries
into foundations of human sociality have perhaps the longest history of any
research question in the human sciences (Hobbes 1958, Darwin 1874, Durkheim
1958, Marx 1975, Smith 1937, and Weber 1968). Every social science discipline is
founded on some assumptions about the nature of human sociality. Adam Smith rooted human sociality in
self-interested decision-making (with instincts to truck and barter), while
Durkheim and Weber laid a foundation for sociology and anthropology on the
essential “collectiveness” of the species.
In this light, we hope to contribute to the construction of new models of
human behavior and the integration of the social sciences by laying a systematic
empirical foundation that takes into account the dual nature of human
inheritance (genes and culture), and the importance of the social environments
generated by different institutional forms.
Although our
work is primarily of theoretical interest, our findings may contribute both
directly and indirectly to the study of economic development, globalization and
policy intervention. First, all
models of economic development, often implicitly, make assumptions without
empirical basis about the nature of human sociality and economic
preferences. Our work may
contribute to such models by providing not only a firm empirical foundation for
making appropriate assumptions about human cooperation, but may yield some
observable variables that will allow development practitioners to predict how
preferences will vary among individuals and social
groups.
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(The
Cultural Anthropology Program Officer gave prior approval for 5 pages of
Appendices.)
The
following pages provide samples of the actual game instructions that will be
used to administer all of the Core Package. Each researcher will have to adapt these
instructions to fit their particular field site by translating them into the
appropriate local language and adjusting the currency and stake sizes. Researchers will use the method of
back-translation to obtain the best possible game translation. This involves having one bilingual
assistant with no knowledge of the game translate the game into the local
language and a second translate it back, thus identifying problems in
translation. However, prior
research has taught us that participants actually learn the games by listening
to and going through many examples—and many participants may be illiterate. For standardization, researchers will
draw all examples from the same pre-defined list, and record how many are used
in instructing each individual.
Teaching
examples: In both
teaching and testing the participants, researchers will use actual coins and
paper currency to illustrate the game—the initial sums and the appropriate
divisions. By presenting the
mathematics of addition and subtraction visually, these kinds of aids allow
individuals to participate who cannot do addition and subtraction (and
multiplication in the punishment and trust games). If necessary, players can manipulate
piles and count coins or bills in decision-making and
testing.
Show-up Fee
and Stakes: Upon
arriving in the game area, players in the dictator, ultimatum, and punishment
games will receive a “show-up” fee, paid in cash at a rate of approximately 25%
of one day’s wage in the local economy.
It will be made clear to the player that this money is strictly for their
participation in the game, and is not
part of the game. Participates who
fail to pass the required tests of game understanding will still receive the
show-up fee—which makes it somewhat easier to reject them, if the need
arises. The stakes will be set at
roughly equivalent to one day’s minimum wage in the local community. In each site the currency denominations
available for play will include at least eleven possible divisions (including
zero).
Introductory
instructions: All game instructions will begin with a
reminder to those present that participation in the games is completely
voluntary, and that they are free to leave at any time if they are uncomfortable
with any aspect of the games.
Given that the populations we work with are not generally familiar with
such exercises, the anonymity aspect of their behavior in the game will be
especially stressed, as will the fact that that they are not taking money away
from the researcher personally through their
participation.
DICTATOR
GAME (CORE EXPERIMENT 1)
Game
Instructions:
This game is
played by pairs of individuals—Player 1 and Player 2. No one knows with whom they are playing
and they never will know, though it is someone from this group/village. The game administrator will provide $50
to each pair. Player 1 will decide
how to divide the money between himself or herself and Player 2. Player 1 must offer between 0 and the
total $50 to Player 2. Player 1
takes home whatever he or she does not offer to Player 2, and Player 2 takes
home whatever Player 1 offers them.
Post-Game
Questions Immediately following the Play:
What would
this game make most people around here think of?
Name this
game: what is a good name for this
game?
What would
most people give to the second person?
STRATEGY
METHOD ULTIMATUM GAME (CORE EXPERIMENT 2)
Game
Instructions:
This game is
played by pairs of individuals—Player 1 and Player 2. No one knows with whom they are playing
and they never will know, though it is someone from this group/village. The game administrator will provide $50
to each pair. Player 1 must decide
how to divide the sum by making an offer to Player 2. Player 1 must offer between zero and the
total $50 to Player 2. Player 2
will then wait while their offer is presented to Player 2. Before hearing the offer made by Player
1, Player 2 must decide what the minimum amount is that they will accept. If the amount offered by Player 1 is
equal to, or greater than, this minimum amount, both players receive the amounts
decided by Player 1. But, if the
amount offered by Player 1 is less than Player 2’s minimum acceptable amount,
then neither player receives any money—both players get nothing. (If the locale requires the range
method, the protocol will be slightly revised.)
Post-Game
Questions Immediately following the Play:
What would
this game make most people around here think of?
Name this
game: what is a good name for this
game?
What would
most people give to the second person?
What is the
minimum most people would accept?
THIRD PARTY
PUNISHMENT GAME (CORE EXPERIMENT 3)
Game
Instructions:
There are
three players in this game—Player 1, Player 2, and Player 3. No one knows with whom they are playing
and they never will know, though it is someone from this group/village. The game administrator will provide $50
to Players 1 and 2. Player 1 will
decide how to divide the money between himself or herself and Player 2. Player 1 must offer between 0 and the
total $50 to Player 2. Player 2
will receive the amount decided by Player 1. Player 3 is told how Player 1 divided
the money with Player 2. Player 3
receives $25, and may pay any amount between zero and $25 to subtract money from
what Player 1 decided to keep for his or herself at a rate of three to one. Thus, for every dollar that Player 3
pays, Player 1 loses $3. Player 2
still receives whatever they were allocated by Player 1.
Post-Game
Questions Immediately following the Play:
What would
this game make most people around here think of?
Name this
game: what is a good name for this
game?
What would
most people give to the second person?
What is the
minimum most people would accept?
If Player 1
gave only $10 to Player 2, how much would most people pay to take money away
from Player 1, if they were Player 3.
TRUST
GAME (FOR USE BY THE NETWORK
ANALYSIS SUB-GROUP)
Game
Instructions:
This game is
played by pairs of individuals—Player 1 and Player 2. No one knows with whom they are playing
and they never will know, though it is someone from this group/village. The game administrator will provide $50
to Player 1 and another $50 to Player 2.
Player 1 has the opportunity to give any portion of their $50 (between
zero and the total $50) to Player 2.
Whatever amount is passed to Player 2 will be tripled by the
administrator before it is passed on to Player 2. Player 2 then has the option of
returning any portion of this tripled amount to Player 1. At this point the game is over. Player 1 goes home with whatever he or
she kept from the original $50, plus anything returned to them by Player 2. Player 2 goes home with their original
$50, plus whatever was passed to them by Player 1 after being tripled by the
administrator, minus whatever they returned to Player 1.
Post-Game
Questions Immediately following the Play:
What would
this game make most people around here think of?
Name this
game: what is a good name for this
game?
What would
most people give to the second person?
If given the
full amount by Player 1, how much would most people return to player
1?
PUBLIC GOODS
GAME (FOR USE IN DOUBLE BLINDS AND CONTEXT
MANIPULATION)
Game
Instructions:
You are
playing a game with five players.
The game administrator will give each of you $10. Each player can invest
any part of that money (from zero to $10) in a “group project.” Each of you will go in private 1 by 1
and put your contribution in an envelope.
No one in the group will know how much you have contributed. All of the envelopes will be mixed up
and then opened. Whatever is in the
envelopes after everyone’s contributions have been put in will be doubled by the
Administrator, and distributed equally among all five players. You will take home whatever you did not
put in the envelope as well as your share of the contributions to the “group
project” once these are doubled by the administrator.
Post-Game
Questions Immediately following the Play:
What would
this game make most people around here think of?
Name this
game: what is a good name for this
game?
How much
would most people around here contribute to the “group
project”?