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Combining Ethnography, Game Theory and Simulation: A Case Study in Theory
Building
Edmund Chattoe-Brown
Department of Sociology
University of Leicester
University Road
Leicester
LE1 7RH
ecb18@le.ac.uk
http://www.le.ac.uk/sociology/staff/ecb18.html
Abstract
In a 2007 paper in Sociology, Gezelius offers a stimulating account of information exchange
between Norwegian fishermen using game theoretic analysis of ethnographic data. In this
paper, I will consider what his analysis reveals about the use of game theory in sociology. To
be effective, the approach must combine systematic use of ethnography with an effective
understanding of different types of game theory. In particular, the relatively novel methods of
simulation and evolutionary game theory are an important adjunct to explanations for the rich
sets of norms and practices observed ethnographically.
Keywords: Collective Action, Ethnography, Functionalism, Game Theory, Norms,
Simulation.
Introduction
In an intriguing paper, Gezelius (2007) analyses information sharing between fishermen in
Norway using game theory to interpret ethnographic data. In this paper, I will consider what
his analysis reveals about the application of game theory to sociology. i I have organised the
discussion in four sections. In the first, I consider problems with the analytical framework
presented by Gezelius and show how existing game theory can address these and, in the
process, do greater justice to his ethnographic findings. In the second, I consider the treatment
of norms provided by Gezelius and argue that while his analysis has weaknesses, the game
theoretical approach he advocates can make a valuable contribution to our understanding of
the relationship between normative and instrumental action. In the third, I consider a general
problem with game theory (how we know which game participants are actually playing) and
show how the ethnographic data which Gezelius presents attempt to tackle this issue but do
not entirely succeed. Nonetheless, I argue that a combination of relevant ethnographic data
with appropriately chosen models can make game theory applicable in sociology. In the
fourth section, I consider the implications of the ethnographic data presented by Gezelius but
not incorporated into his analysis, in particular the possible consequences of prestige
competition. The final section concludes.
Not My Type?
Gezelius discusses information exchange between fisherman (about good fishing sites) using
game theory. The key insight of this approach is that choices made by each actor in a social
setting impact on the outcomes for other actors: Society does matter. In particular, probably
the best known finding of game theory is the existence of collective action problems (CAP)
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with the Prisoner’s Dilemma (PD) as the paradigm case. In a CAP, two (or more) rational
actors reasoning selfishly achieve a less satisfactory outcome than they could have achieved
had they not been rational and selfish.ii Since much action (even in markets) does involve the
interplay between actions and outcomes (for example production and global warming), this
simple example is a significant counter to the neo-liberal ideology that the economic system
will translate individually selfish and rational behaviour into collective welfare. For the
fishermen, the best situation is that everyone shares information, but the existence of a
“sucker” outcome (I do best if I get you to share while I do not) means that nobody shares.
The first issue with the analysis presented by Gezelius is technical but important for what
follows. The PD is a simultaneous game. Each player reveals their action at the same time and
then outcomes occur. This means that playing the game repeatedly involves a sequence of
discrete interactions which can terminate at any time. In order to participate, each player only
needs a way of deciding what to do. S/he needs no information about the future. Gezelius
presents the same set of payoffs to actions that characterise the PD but as a sequential game.
First one fisherman decides whether to share and then the other. Payoffs are only achieved
after a pair of sharing decisions. This raises a problem about how fishermen would actually
play. Obviously, if they are certain that the game will involve only two sharing opportunities
(one each) then both have a clearly defined rational non-co-operative strategy (as Gezelius
shows). However, what is to stop two opportunities for fisherman X helping fisherman Y to
arise before Y gets a chance to reciprocate? If payoffs are only achieved at the end of some
sequence of actions – as Gezelius specifies – fisherman have to consider all possible future
sequences (of all possible lengths) before deciding what to do. This seems highly implausible.
However, let us leave this problem aside and suppose that the information sharing game is
simultaneous so that rational action is easily implemented. How do fishermen actually play
repeatedly? Gezelius presents several ways of doing this but fails to discuss either their
significance or why he has chosen to present the strategiesiii he has. The issue is that when
agents face a CAP repeatedly, one way of “solving” it (avoiding socially disadvantageous
outcomes) is to use actions to “reward” or “punish” actions by co-players and thus raise the
average payoff in a sequence of games. In particular, if players “reward” co-operative actions
(leading to the best collective outcome) and “punish” defecting actions (leading to the worst),
then they can sustain co-operation even when players are rational and selfish.
A well-known example of such a strategy is called Tit-For-Tat (TFT). When a player meets a
new co-player for the first time, s/he always co-operates. After that, the player does what the
co-player did last time they played. This means that TFT players cannot lose out in the long
run (to players who always defect for example, the so called ALLD strategy) but will be able
to sustain co-operation when they meet other potential co-operators. TFT became widely
known because it was both very simple and surprisingly effective at sustaining co-operation
in a population where actors played according to different strategies (Axelrod 1984).
However, there are a very large number of possible ways of deciding what an agent should do
next. The simplest are the strategies like ALLC and ALLD (which ignore history altogether)
but most strategies map actions by your co-player in previous periods to your action in this
period. (For an interesting analysis of such a system, see Lomborg 1996).
Gezelius describes several strategies for the PD (like Tit for 2 Tats – TF2T – and Grim
Trigger – GT) but does not say why these are relevant to the fishermen either empirically or
theoretically.
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What are the implications of these objections? The first is that analysis of the repeated PD (a
simultaneous game) is not appropriate to a sequential game. Even though TFT is an effective
way of playing the repeated PD, it does not follow that it is an effective way of playing
sequential games. The second is that Gezelius fails to answer several questions on which his
analysis appears to hinge: How do fishermen actually play this information sharing game
repeatedly, how do we know this and why do they play as they do? In order to provide a
possible answer to these questions I need to discuss two more game theoretic concepts.
TFT, ALLD and so on are what game theorists sometimes refer to as “types” and they assume
that these are deterministic. As such, they may not be very good representations of
sophisticated, reflexive human actors. On the other hand, there are reasons for thinking that
humans may act in these simple ways. They may be socialised thus, it may save mental effort
in routine situations and so on. These are arguments in favour of bounded rationality (Simon
1976) which emphasises routine behaviour according to “rules of thumb”. The success of TFT
results from its performance relative to other strategies in terms of average payoffs for
repeated play with recognisable co-players. If we allow that there are some patterns in the way
that real agents play games (not necessarily deterministic but still identifiable to others) it
follows that other agents are advantaged if they can spot those patterns (carry out “type
identification”) and respond accordingly.iv
It is here, however, that simple game theory gets into conceptual difficulties. If types really
are deterministic then players might identify certain other types easily enough but would not
be in a position to do anything about it. It is easy to recognise an ALLC because they never
defect but, if you are an ALLD, you cannot change your response based on that knowledge
because it is determined ex hypothesi. Similarly, if you need to “experiment” (by changing
what you play), to distinguish a TFT from a TF2T for example, then your determined
behaviour does not allow this and, even if it did, you would then confuse others who were
trying to infer your type. (They would not know if your last co-operation was a true
representation of your underlying type or an experiment.)
The ethnographic data that Gezelius offers suggests a way out recognising that the simplistic
idea of deterministic types will not do either conceptually or empirically. What he observes is
that a particular action (lying) almost never occurs and that a particular sequence of actions (I
help you after you help me) often does. What happens, he suggests, is that violation of these
norms doesn’t just change the way the game is played with that co-player but also introduces
choice and refusal of partners (Stanley et al. 1994). Lying appears likely to put you
completely outside the group while single instances of non-reciprocation may be forgiven
without affecting group membership.v This introduces network structure into game play. Each
fisherman is not equally likely to share information with every other as Gezelius shows. Thus,
the real social context replaces deterministic types with “moral” types (liars and free riders).
Instead of a concern with identifying types like TFT, the social significance of the “liar” label
rests only on the rather minimal behavioural presumption that what a person has done once
they may do again. By addition of some new game theory and clarification of the framework,
we have got further in understanding what needs to be explained. Fishermen display (and
monitor) certain behaviours which the group regards as normatively unacceptable. The
punishment for this is withdrawal of economic rewards (catch sharing), network connections
(information sharing) and social status (ability to compete for prestige). In addition,
information about norm violations (reputation) is presumably spread through social networks
in the form of gossip. However, we still don’t know how this particular set of norms and ways
of playing the information sharing game came about or whether it is “stable”. In particular, is
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the fact that lying is regarded as so serious merely a historical contingency or crucial to the
maintenance of the system? I shall suggest one way we might start to answer this question in
the penultimate section.
What’s in a Norm?
I have already discussed how repeated interaction can sustain “self interested” co-operation
despite CAP. It could thus be objected that Gezelius has failed to explain why he needs to use
norms to explain co-operation at all. However, the advantage of ethnography is that he can
point directly to normative talk about information sharing which needs to be accounted for.
Nonetheless, the interpretation of norms he proposes is problematic for reasons that need to
be discussed if we are to provide a better account of the interplay between normative and
instrumental action found in most social situations.
There are actually several “solutions” to CAP. As well as the repeated play solution described
above, the game can be rendered non-problematic – by punishing defection and rewarding cooperation externally (fines for littering). This changes the payoffs and therefore the “best”
individual actions, bringing them into line with the best collective outcome. Alternatively,
without introducing external monitoring, participants can institute a social practice and
associated norm (no “grassing”).
The difficulty arises over the status of “internal” norms. If, as game theory assumes,
individuals are purely self-interested, it is not enough for a norm to exist to explain why it is
followed, it must be in the individual’s interest to do so. Only if norms are enforced by
punishment will behaviour be changed and it isn’t clear why selfish individuals would bother
to punish for the common good (Heckathorn 1990). Some sociologists are rightly
uncomfortable with a reduction of all norms to the pursuit of self interest since this seems to
vitiate the concept. The claim that someone has done something because they feel it is right
(rather than because they fear punishment) does not seem patently nonsensical or self-serving.
The question then arises. If norms are not merely more complex forms of self interest, what
are they?
Gezelius argues that norms narrow the set of choices so that information sharing becomes
more prevalent. From the full set of actions that fishermen might take, they actually take only
a subset for normative reasons (pp. 213-214). Unfortunately, this doesn’t address the problem
that Gezelius poses. Do fishermen refrain from lying because they are afraid of the
consequences (whether economic, social or whatever) or do they refrain from lying for
reasons that, in at least some cases, will cause them to act against their interests? There are
two complicating issues here. Firstly, rational choice is always in danger of becoming a
truism. If anything can constitute “utility” then even if an action loses me a fortune, I may still
choose it rationally for the personal (unobservable) “warm glow” I get from doing the “right
thing”. This makes the theory completely general but also useless since any behaviour can be
explained by arbitrary assumptions. Secondly, why do normative constraints simplify the
game proposed by Gezelius in just the way he indicates rather than in some other way? If
norms can dispose of CAP, why not dispose of choice too and simply have a norm that
everyone behaves the same? In order to justify explanations of norms and behaviour that are
not purely rational, we must either explicitly or tacitly appeal to functionalism: The society of
fishermen is this way because it is able to reproduce itself more effectively than some other
arrangement we might have observed but did not. In fact, as Gezelius remarks, lying is not
unavailable as an option, it is just extremely rare so it seems more reasonable to analyse the
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full game and try to explain the scarcity of lying as a behaviour than to argue that the game
has been reduced to a simpler form. For example, once social networks exist, it may be
possible to lie to “outsiders” while avoiding repercussions that would occur within the
community.
Unfortunately, Gezelius has not (as he claims) reconciled rational action with norms but the
reason is that this is actually very difficult to do on two counts. vi Firstly, we need to
demonstrate that a normative action is not simply a more complex form of self-interest (if that
is indeed what we believe). This may be very difficult within the framework of rational choice
because that theory may turn out to be a truism and not empirical if pushed to its limits.
Secondly, we need to give some alternative account of the origin and survival of norms that
allows us to say why we observe one set of norms rather than another. (In this case, for
example, why do norms restrict choice to encourage sharing rather than simply abolishing
choice to guarantee it?) It seems we cannot get away from connected questions about how
these action/norm systems are created and reproduce themselves if we want to explain them.
What’s Your Game?
Having been somewhat critical of the analysis offered by Gezelius, I think his attempt to
combine detailed ethnography with game theory renders his work more valuable and relevant
than most economic analysis. This is because ethnography addresses a key problem which
game theory faces, namely how to establish what game people are playing. Even in
experimental contexts (where this is supposed to be controlled) it is often clear to laboratory
helpers that players see the game differently from the way they are “supposed to”. That said,
however, much of the data discussed by Gezelius has not been explicitly incorporated into his
analysis and there are data missing that would be required for a more accurate specification of
the relevant game.
The most notable issue missing from the analysis is the prestige contest amongst fishermen.
This is described in considerable detail and would appear to be socially significant but
Gezelius offers no analysis of how it might impact on game play. Of particular interest are
two remarks he makes. Firstly, that most boats have a single close comparator that they use
for reference in the prestige contest (p. 210). Secondly, that fishermen who break the rules not
only suffer economic and social costs but are also rendered ineligible for the contest (p. 209).
This seems like an extremely important point about the limits of instrumental and normative
spheres but nothing is made of it. Pre-empting discussion in the penultimate section, what is
interesting about this description is its functionalist implications. What we appear to have here
is a norm system that rewards performance within a particular framework (no lying and
significant reciprocation) and the outcome is likely to be creative innovation and greater
collective effort in pursuit of prestige. Far from a CAP, the prestige contest and associated
normative “protection” for rewards to fishing skill rather than ability to lie or free ride, might
well benefit the whole community.
Other missing and non analysed data are more minor but still important to understanding the
game fishermen play:
1) Do fishermen talk about their own fishing sites (making the game one of collective action
as Gezelius argues) or do they only “give away” sites they pass en route or believe
inferior?
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2) How much of a CAP is there? Gezelius mentions the direct detection of fish and
observation of other boats as fish location strategies (pp. 206-207) but he doesn’t indicate
their relative importance. Perhaps information exchange only occurs because the bulk of
the catch is secured by other means and it is therefore trivial?
3) Both the terms “hint” and “restricting information” are crucial to the specification of the
action set for the game but are not really discussed. What does it mean to provide a hint?
Something which is not certain to lead to finding the fish? Something which can more
easily be interpreted by the intended recipient? How do we specify a game in which
players don’t take discrete actions but offer information varying in specificity and
interpretability?
4) To what extent do novel strategies or actions play a part in the game? Gezelius mentions
one example of a novel action (p. 212) but is innovation common? Does the ability to
innovate affect the rules by which the game is played? Are some new strategies/actions
ruled normatively unacceptable after their first use or even before? What effect does it
have that older skippers or those from outside may use strategies/actions not known to
locals or the less experienced?
5) Given the rarity of lying, how is the norm against it transmitted and maintained? Unlike
many game theoretic models, fishermen are not learning to respond to this situation by
direct experience because it is too rare. What is the “character” of the reputational gossip
in the community? Does it warn as well as regulate?
6) The ethnographic data describes a range of benefits and sanctions operating within the
community but the game specification deals only with information sharing. It is not clear
whether this reflects the relative importance of this aspect of the system or not. For
example, how does being “bona fide” impact on the ability to acquire surplus catch from
others?
7) While it is not fair to chide an ethnographer from the armchair for data they have not
collected in the wind and rain, the detailed social network structure of information
exchange may be necessary to explain important observations. In particular, Gezelius
presents the very interesting finding that, in conditions of scarcity, fishermen do not
change the way they play the game but they do narrow the scope of potential co-players to
those who are “closer” (p. 211). This is a clear example of choice and refusal of partners
but what is its logic? Does this norm distribute scarce catches more fairly, encourage more
intense competition to maximise catch in difficult circumstances or is it simply a cultural
contingency?
The conclusion of this section is that game theoretical analysis and ethnography can be a very
powerful combination provided the theorist has a broad enough palette of models at his or her
disposal. If not, the danger is that ethnographic data will be “cherry picked” to justify a
particular model in an ad hoc way and this may raise conceptual and analytical difficulties of
the kinds discussed above. Fortunately, qualitative research already has inductive coding
(Strauss 2003) as a strategy for ensuring that theories are well grounded in data and the same
logic should apply here. Only after the situation and the possible strategies have been clearly
defined from the ethnographic data should the appropriate game structure be specified. At the
same time, identification of a potential game theoretic explanation for a social phenomenon
may help to shape the kinds of ethnographic observations that need to be made. (Here, for
example, we would need to examine the nature of hints and gossip in more detail after game
theoretic analysis had suggested itself.)
What’s the Payoff?
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In previous sections, I have established a clear analytical framework, identified possible roles
for norms in sustaining co-operation and considered how game theory and ethnography could
work together. However, there is a final “loose end” arising from this discussion to which I
have alluded several times. If we cannot plausibly account for the behaviour and normative
views of fishermen in terms of calculating self-interest, how do we account for it? How can
we say something about why particular sets of norms and behaviours exist in particular social
contexts? One answer can again be found in game theory but of a distinctive kind. Although
Gezelius doesn’t make it clear, the reason why game theorists are particularly interested in
strategies like TFT is because they have developed an “evolutionary” account of the way
games come to be played. In a population of agents using different strategies, some will
achieve higher average payoffs than others. For example, TFT will do better on average than
ALLC because ALLC can be “exploited” (end up suffering a sequence of “sucker” payoffs
without reacting) if it meets ALLD while TFT cannot. If we assume that survival is associated
with the average payoff of a strategy, then we have an evolutionary/functionalist account that
explains why some strategies are observed and others aren’t. If a strategy does relatively well
it will end up with more “copies” in the population (either by survival/physical reproduction,
deliberate learning or behavioural imitation) and if it does relatively badly it will end up with
less. Thus, one way we can explain the prevalence of a strategy (or a set of norms and
practices) is by its ability to reproduce itself. Thus in observing that lying is rare and
reciprocity common amongst Norwegian fishermen, we might be saying one of two things.
The first possibility (already identified as implausible) is that the structure of the game is such
that fishermen calculate that lying is imprudent and reciprocity prudent. The second is that
fishermen who happen to lie (for whatever reason) are rather likely to be “weeded out”
(nobody follows their example, they go bankrupt or move away) while those who reciprocate
and are honest get access to economic/social benefits thus becoming relative successes
(perhaps even role models). Gezelius shows how we should develop a theory of the first kind.
We specify the game accurately using ethnographic evidence (which is why systematic
analysis is important to the correct outcome) and work out what the rational strategies are. To
explore a theory of the second kind, unless we want to make the excessively strong
simplifying assumptions needed for mathematical analysis, we need to use the novel
technique of computer simulation (Gilbert and Troitzsch 2005).
In this case, the approach is to begin with a diverse population of “social practices” and use
simulation to explore the dynamic process by which a subset of these (or perhaps even a
single one) comes to dominate the population. For now, to simplify explication, I shall equate
social practices with game theoretic strategies already discussed but later I will consider the
more realistic case presented by Gezelius. If a particular strategy like TFT becomes dominant
in the face of a wide range of other strategies and details of game play (like how good agents
are at recognising each othervii) then we may be somewhat more confident that TFT is a
“robust” strategy that we might expect to find in real social settings. If, on the other hand, the
success of a strategy depends very precisely on the initial population and the way the game is
played, we might not expect to observe it in the real world. This is quite an interesting
perspective on functionalism. What we are saying is not that the world is tending in a
particular way (towards the nuclear family for example) but that certain ways of operating
socially appear to be more likely to survive the details of the social context and reproduce
themselves than others do. That is not to say that very non-robust social arrangements might
not survive in specialised, static or isolated settings but rather that “generic” social
arrangements might be widely distributed precisely because they are relatively robust to
environmental detail.
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FIGURE 1 here
Figure 1 above shows a simple simulation of interacting strategies written in NetLogo. viii The
sliders show the populations of different strategies displayed on the right hand screen as
colour coded arrows, in this case ten agents who play RANDOM (light) and ten who play
TFT (dark).ix Agents move around the space randomly until they meet and then play a single
PD according to their respective strategies, updating their histories of interaction and average
payoffs. The graph at the bottom left shows that while the RANDOM strategy initially does
better (dark line), it is soon dominated by TFT (light line). This is because TFT agents can
learn mutual co-operation while RANDOM agents cannot capitalise on the fact that TFT is
potentially co-operative.
By contrast, when we have twenty agents each for five different strategies RANDOM, ALLC,
ALLD, TFT and UNFORGIVING, we find that (in a steady state) RANDOM does worst,
then ALLC, then ALLD, then TFT and UNFORGIVINGx does best. In this environment, TFT
is neither the best strategy nor strikingly better than the next best. Table 1 shows below how
the payoff rank changes over time. This is an example of the way in which the details of play
affect strategy success. The average payoffs to each strategy depends on the extent to which
they are able to learn to cooperate with other strategies presently in the population. If agents
pursuing relatively unsuccessful strategies were more likely to be removed from the simulated
space and those pursuing successful strategies were more likely to reproduce themselves in
new agents strategies like ALLD would probably start to do worse. This might occur as the
population of ALLC that they “exploit” declined and the populations of TFT and
UNFORGIVING (against both of which ALLD does relatively badly) grew.xi We can thus see
how, generally, it is not possible to say that a strategy is absolutely good but only that it does
relatively well against a particular population of other strategies. (“Robustness” means that it
does well in a very wide range of mixtures of many strategies.) Furthermore, with death and
birth of agents, a highly co-operative solution may make the population very vulnerable.
Suppose that the dynamics of the system make ALLC universal. Although very high levels of
co-operation would then occur, this would be undermined by the phenomenal success of a
single ALLD reintroduced at a later date. Thus, when we look at the robustness of a strategy
like TFT, we are not only looking at payoff but also the ability of the strategy to “resist”
incursions of novel strategies or those previously driven extinct (Axelrod 1984). To return to
Gezelius then, the argument is that norms and practices we observe in real social settings can
be “explained” by their robustness in suitably simplified simulations of those settings. The
logic of this is that, as a social science, sociology wishes to focus on those aspects of sociality
that generalise rather than being highly contingent.
TABLE 1 here
Simulation is not only a suitable method because it can give a general evolutionary account of
strategies but also because it can represent the details of particular social settings like a
fishery and their bearing on the norms observed, albeit in a simplified fashion. Although the
examples above assume random movement on a featureless space for simplicity, it would be
straightforward to add the movement of fish shoals/boats and the selling of catches on return
to a particular location.xii In this way, rather than having to specify a very complex game tree
and reasoning process, the costs and benefits of different strategies and actions would be
explicitly realised in the simulated environment. (For example, the receipt of surplus catch
would depend on physical proximity at the time of the catch, being regarded as “bona fide” in
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the community and being near the top of the list for “a favour”, a mixture of geographical,
social and individual “causes”.)
At this point, it is necessary to consolidate the argument somewhat. Because of the game
theoretic approach adopted by Gezelius and the need to present simple examples of a novel
approach such as simulation, I have tended to focus fairly narrowly on strategy evolution.
However, one of the issues I have raised is the coherent treatment of norms and, to make
progress in this direction, I need to sketch a less simplified view of social evolution.
The key elements of this view are the importance of socialisation and a recognition that social
evolution is compatible with both rationality and underlying biology. I specifically do not
want to open cans of worms regarding how much of our behaviour is biologically determined
and how much can be explained as the outcome of successful deliberation. All my argument
needs is that some behaviour is neither rational nor biologically determined. I doubt any
sociologist would object to that claim. Given this, we can see how socialisation can involve
actions (“don’t kick”), norms (“don’t talk to liars”) and roles/institutions (“do what the boss
tells you or you’ll get the sack”). Over time, social innovation, “human nature” (biology),
learning (whether individual, social or imitative) and rational attempts at improvement (if
successful) can all shape what is passed on through social reproduction. All that we have to
accept is that some combination of environmental pressures external to the socialisation
process may affect it to see how different “complexes” of norms, actions and beliefs may be
more or less able to persist. Thus, in trying to understand the activities of Norwegian
fishermen, we cannot limit ourselves to an analysis of behaviour (“share”, “don’t share”) but
must also consider the details of the belief system into which they are socialised. It is quite
possible that this belief system (with associated norms about information sharing and truth
telling) is beneficial in dealing with problems that may arise through CAP.
Fortunately, although I have illustrated the use of simulation in understanding social evolution
with the simplest possible examples, the method has no difficulty in modelling the evolution
of norms as well as behaviours (Flentge et al. 2001). However, quite apart from the
practicalities of article length, building a simulation based on the article by Gezelius (given
the gaps in data and conceptual problems raised above) would do little to strengthen my
argument so I have not attempted it. Nonetheless, I hope I have shown how simulation could
be used as part of a programme of research combining ethnography and game theory. Having
gathered ethnographic data about the nature of the game and the way it is actually played, the
simulation could be used to “experiment” with the sensitivity of the observed outcome to
various details (in the example shown above, different sets of strategies in different
proportions) to understand the extent to which a particular set of norms, actions and
institutions is highly contingent or relatively robust. If it is robust, it may at least explain the
actual observation of these phenomena.
The final issue to consider, which I have already raised, is the possible functionalist role of
prestige contests. This is now much easier to understand in the context of the discussion
above. Many evolutionary game theory accounts look at the literal removal of agents who
perform relatively poorly. In biological models this is justified in terms of Darwinian
selection (the agent “dies”) while in more social settings it may involve learning. The agent
doesn’t necessarily disappear but the strategy does. However, in the context described by
Gezelius, it does not appear to be the case either that unsuccessful boats disappear completely
or that skippers adapt their strategies explicitly to those of more successful boats. Instead, it is
the prestige hierarchy that directs the evolution of behaviour more effectively to collective
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success than a process based on mere survival (which might suffer from CAP). Thus, instead
of undirected selection, the system involves the creation of norms and practices for directed
selection but rather than this direction coming about via rational deliberation at the individual
level (which suffers from CAP), it is the communal selection mechanism itself that may be
socially sustained and reproduced by its functional advantages. This is a testable hypothesis
using simulations (of the kind discussed and presented above) and involving a potential
innovation in game theory: Does some socialised system of evaluation (which may include
praise, blame, choice and refusal and “prestige”), combined with game play, sustain both
itself and co-operation better than “simple” selection or learning of actions with no
evaluation? If so, we have a potential functionalist explanation for the existence and
maintenance of prestige contests (and generally for the normative sphere) as a novel
counterweight to certain kinds of CAP which cannot be solved at the individual level. Further,
we have an insight into a method (computer simulation) which might allow us to investigate
hypotheses of this kind by experimenting with the robustness of such evaluation systems to
the details of the social setting over time. To recap, if we find that such systems can survive
and reproduce themselves in a variety of simulated contexts, it is reasonable to expect them to
exist in real societies.
Conclusion
After covering quite a range of different aspects of the data and analysis presented by
Gezelius and introducing several game theoretic tools, we are now in a position to pull the
argument together and reach conclusions about the potential role of game theory in sociology.
The approach advocated by Gezelius improves on both economic analyses using game theory
and much qualitative sociology in developing formal theory from naturalistic observation.
Ethnographic data solves the serious problem, faced by “pure” economic analysis, of deciding
which game actors are playing. However, the analysis offered by Gezelius suffers because he
does not consider a broad enough range of game theoretical models to find the best match
with his data and because systematic analysis of his data does not appear to have been carried
out through the “lens” of his theoretical approach. This means both that some aspects of his
analysis are not supported by his data (like the use of the GT strategy against liars) and that
some of his data do not feature in his analysis even though they are prima facie important
(like the prestige contest and networks). However, as I hope I have shown in this paper, both
of these methodological weaknesses can be remedied using existing tools from game theory
(types/strategies, choice and refusal, evolutionary games) and qualitative analysis (inductive
coding).
As the obverse of my critique, I have also shown the potential of game theory as a set of tools
that can be applied appropriately in different contexts to yield interesting insights. Thus we
see that the evolutionary approach to game theory (augmented by the technique of computer
simulation) can tie together many of the different issues raised by Gezelius into a single
coherent framework. Ethnography is used to identify the game, strategies and details of the
social setting and simulation is used to explain how, in that particular setting (suitably
simplified),xiii the strategies observed may have come to be the dominant ones. In particular,
the potential functional role of the prestige contest can easily be understood within this
framework as an evolved “complex” of norms and practices for ensuring innovation and
effective (rather than socially damaging) competition.
[gtfishweb.doc]
11
Gezelius has shown the potential of this approach but it is clearly possible to go a lot further
both empirically and theoretically without exhausting the potential of combinations of game
theory, simulation and ethnography in explaining the “complexes” of norms and strategies
operating in particular social contexts. I hope this paper will go some way to encouraging
interest in the development of such a systematic research programme.
References
Axelrod, R. (1984) The Evolution of Cooperation. New York, NY: Basic Books.
Flentge, F., D. Polani and T. Uthmann (2001) ‘Modelling the Emergence of Possession
Norms using Memes’, Journal of Artificial Societies and Social Simulation, 4(4):
<http://www.soc.surrey.ac.uk/JASSS/4/4/3.html>.
Gezelius, S. (2007) ‘Can Norms Account for Strategic Interaction? Information Management
in Fishing as a Game of Legitimate Strategy’, Sociology, 41(2): 201-18.
Gilbert, N. and K. Troitzsch (2005) Simulation for the Social Scientist, second edition.
Maidenhead: Open University Press.
Hägerstrand, T. (1965) ‘A Monte Carlo Approach to Diffusion’, Archives Européennes de
Sociologie, 6(1): 43-67.
Heckathorn, D. (1990) ‘Collective Sanctions and Compliance Norms: A Formal Theory of
Group-Mediated Social Control’, American Sociological Review, 55(3): 366-84.
Lomborg, B. (1996) ‘Nucleus and Shield: Evolution of Social Structure in the Iterated
Prisoner’s Dilemma’, American Sociological Review, 61(2): 278-307.
Moulin, H. (1986) Game Theory for the Social Sciences, second revised edition. New York,
NY: New York University Press.
Petersen, T. (1994) ‘On the Promise of Game Theory in Sociology’, Contemporary Sociology,
23(4): 498-502.
Simon H. (1976) ‘From Substantive to Procedural Rationality’, in S. Latsis (ed.) Method and
Appraisal in Economics, pp. 129-148. Cambridge: Cambridge University Press.
Stanley, E., D. Ashlock and L. Tesfatsion (1994) ‘Iterated Prisoner’s Dilemma with Choice
and Refusal of Partners’, in C. Langton (ed.) Artificial Life III, pp. 131-175. Reading, MA:
Addison-Wesley.
Strauss, A. (2003) Qualitative Analysis for Social Scientists. Cambridge: Cambridge
University Press.
Notes
i
This paper is distinctive in being a detailed case study of real (albeit previously published) research rather than
a general prospectus for sociological game theory (Petersen 1994) or an exposition of methods (Moulin 1986).
ii
For a typical PD, the payoffs for all the possible action combinations by both players [A, A], [A, B], [B, A] and
[B, B] might be [£3, £3], [£0, £4], [£4, £0] and [£2, £2]. Both players observe that, regardless of what the other
player intends to do, action B yields a better payoff for them than action A. In consequence the action
combination that occurs is [B, B]. Note, however, that [A, A] both delivers more income to each individual than
[B, B] but also more income collectively than any other action combination. Players are selfish because they only
take their own utility into account. They are rational because they intend to maximise their own utility over the
set of possible actions.
iii
A strategy is a systematic procedure for determining what action to take in a game (or game sequence). It is a
description sufficient to allow a “proxy” to play for you. “Always play C” is thus an example of a strategy.
iv
If I know in advance that someone is an ALLD type for example then (if I have any choice at all) it makes no
sense to play C with that person.
v
Unfortunately, this is a concrete example of the problems that arise from the unclear conceptual framework
presented by Gezelius. He claims (p. 213) that lying results in subsequent use of the GT strategy against the
[gtfishweb.doc]
12
offender, which differs from TFT in never returning to C after a C from the co-player. However, refusing to play
with someone at all is clearly not the same as always playing D with them.
vi
Confusingly, having rejected policing and sanctions early in the paper (p. 204), he then discusses at length
exactly what sanctions and rewards operate within the community! (p. 209-212).
vii
If I forget I have met someone already as a TFT then I may cooperate when, based on past information, I
ought to defect.
viii
The simulation model from which this screen shot was obtained is copyright Uri Wilensky 2002 and is freely
reproduced within the terms of permission granted at <http://ccl.northwestern.edu/netlogo/models/PDNPersonIterated>. NetLogo can be downloaded free for several different operating systems (notably MacOS and
Windows) from <http://ccl.northwestern.edu/netlogo/>.
ix
Other buttons initialise the simulation with the number and kind of agents specified and start and stop the run
while the simulation also displays the payoff structure of the game for reference and records the elapsed time.
Note also that the agent screen displays payoffs for agents who are playing the game in that period.
x
UNFORGIVING may or may not be exactly the same as the GT strategy described by Gezelius as there are
minor behavioural variants but its key feature is that once a particular co-player defects, the agent will never cooperate with that co-player again.
xi
Such interpretations must be made with caution (and are only offered as examples) as it is here that intuition
fails us and we therefore need simulation. The overall effect of the dynamic process with populations of several
strategies is hard to infer from knowledge about how the strategies interact on a pairwise basis.
xii
For an interesting example dealing with geographical features using real data see Hägerstrand (1965). To show
that simulation has no difficulty modelling fishing behaviour explicitly in NetLogo, see
<http://ccl.northwestern.edu/netlogo/models/community/Fishery>.
xiii
The specification of the simulation can also be used to identify ethnographic and analytical questions: Do fish
shoals move so that honest information exchange can still fail to produce payoffs? Under what circumstances (if
any) do non-reciprocating skippers regain their “bona fide” status?
[gtfishweb.doc]
13
Figure 1. A Simple Simulation of Interacting Strategy Types
Strategy
ALLC
ALLD
TFT
UNFORGIVING
RANDOM
20
5
1
3=
3=
2
40
3
1
5
4
2
60
4
1
5
3
2
500
5
1
4
3
2
1000
5
1
3
2
4
Number of Periods
2000 3000 4000
5
5
4
1
2
3
3
3
2
2
1
1
4
4
5
10000 20000 33400
4
4
4
3
3
3
2
2
2
1
1
1
5
5
5
Table 1. Ranking of Different Strategies by Average Payoff Over Time