Life with Norm Entrepreneurs

In contrast to the dichotomous patterns exhibited when the system lacks norm entrepreneurs, their presence creates different patterns. First, norm entrepreneurs are able to influence which rule rises to dominant status when the noise/precision levels would otherwise lead to stability around

Figure 4.4

Population Predictions—Low Noise, Entrepreneur Present

10 Agents, 6% noise, 5% precision o

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401 501 Round

the dominant rule. See Figure 4.4 for a demonstration of this effect. The simulation depicted in Figure 4.4 is similarly configured to the simulation run in Figure 4.2, except that a norm entrepreneur is now present.

The impact of the norm entrepreneur was significant. The agents still crystallized around a single rule for the majority of the simulation, but instead of the dominant rule 4, the agents crystallized around rule 1 (which returns a prediction between 0 and 10) after the suggestion of the norm entrepreneur. The norm entrepreneur was able to alter the manner in which the agent population crystallized around a single rule—a rule that generates a prediction far different from the otherwise dominant prediction that hovers around 50. Repeated trials demonstrated that any of the rules can rise to normative status under these conditions.32

Here we see that when agents can clearly discern what is appropriate (a situation of low social complexity), the norm entrepreneur can have a huge effect on norm emergence (though as per the above results, the norm entrepreneur is not necessary to catalyze norm emergence). The norm entrepreneur's suggestion locked the population into a particular norm (and it was not the intrinsically attractive rule). This pattern is reminiscent of technological lock-in discussed in the increasing returns literature in economics where a firm or even an individual offers a new technology that can rapidly become the entrenched standards.33 The QWERTY keyboard34 and the VHS VCR35 are classic examples. Entrepreneurs (or even historical accident) provide the initial advantage to a rule (a few more VHS ma chines, or a few more QWERTY type-writers) in an environment with few extant alternatives, and the positive feedback or increasing returns built into the model and drawn from the constructivist framework act to lock-in the rule.

Politically speaking this could represent institutional lock in or the role of power. The U.S. Constitution is a good example of institutional lock in. The U.S. state could have been organized in any number of ways—but there were few practical alternatives in the beginning. The Articles of Confederation was one potential idea that faded quickly, and entrepreneurs (the framers) presented a new notion of political organization. This new idea gained currency quickly and became locked in, impervious to other suggestions. Powerful entrepreneurs can have the same effect. For instance, when a dominant power in international relations makes a suggestion, there may be little noise associated with it. This effect is amplified in certain conditions, for instance when a hegemon makes a suggestion at the conclusion of a war.36 The setup of the Bretton Woods institutions after World War II is a pertinent example. The social complexity surrounding the configuration of the global economy was very limited and overwhelmingly influenced by the United States.37

Lock-in is not the only effect that norm entrepreneurs can have on the system. At higher levels of noise, entrepreneurs catalyze metastable patterns in contrast to a strict breakpoint between volatility and stability. Norm entrepreneurs allow the system to walk the line between volatility and stability and they create patterns of rising and falling norms over time. Metastable patterns occur when pockets of stability arise but do not last— there is stability in the system but it is not robust. In these simulations, the agents can coalesce around any of the rules and we see the rise and demise of intersubjective agreement among the agents. In essence, the norm entrepreneurs are able to catalyze intersubjective agreement, but the agreement does not dampen the dynamism of the system. Instead, the agreement (or norm) lasts for a while before eroding via agent choices and new norm entrepreneur suggestions. The stability erodes because the system is too noisy to support long-term stability and norm entrepreneurs periodically prod the system with new suggestions. Norm entrepreneurs are thus able to catalyze both norm change and norm evolution.

Figure 4.5 demonstrates the impact of norm entrepreneurs on a simulation similar to the one run in Figure 4.3. Figure 4.6 shows rule usage in the entire population. In this figure, each pattern represents a rule, and prevalence of a pattern represents the percentage of the population that is using that rule in every round. When only one pattern is present (vertically) as happens at round 101, 301, and 701, then all the agents in the population are using a single rule (rule 5 at 101, rule 4 at 301, and rule 2 at 701). The more a pattern is displayed, the more agents are using the rule.

Figure 4.5

Population Predictions—High Noise, Entrepreneur Present

10 Agents, 10% noise, 5% precision

Figure 4.5

Population Predictions—High Noise, Entrepreneur Present

10 Agents, 10% noise, 5% precision

1 101 201 301 401 501 601 701 801 901

Round

1 101 201 301 401 501 601 701 801 901

Round

Figure 4.6 Public Rule Usage in Population

10 Agents, 10% noise, 5% precision

Figure 4.6 Public Rule Usage in Population

10 Agents, 10% noise, 5% precision

1 101 201 301 401 501 601 701 801 901

Round

1 101 201 301 401 501 601 701 801 901

Round

® Rule 1 lI R ule 2 0 Rule'3 3Rule4 ■ Rule 5 SRuleâ 3Rule7

These figures demonstrate that metastable patterns result from the norm entrepreneur's suggestions at a level of noise high enough to cause volatile outcomes in systems lacking an entrepreneur. The norm entrepreneurs catalyze periods of intersubjective agreement among the agents—they make it possible for agents to crystallize around a rule for relatively short periods in an environment that would otherwise lead to volatile patterns.

I suspect that this pattern is representative of the majority of social life, where we see neither permanent lock-in nor constant searching for a norm. Most studies of norms in the constructivist literature detail norm change—

shifting development initiatives at the World Bank,38 changing global environmental attitudes,39 the spread of human rights,40 the downfall of apartheid,41 or the growth of a chemical weapons taboo.42 Finnemore details the evolution of development initiatives at the World Bank—from a focus on raising GNP to poverty alleviation and now onto sustainable development— and points to the role of a norm entrepreneur, Robert McNamara in bringing about the change to poverty alleviation.43 Most social life seems to walk this line between stability and volatility—things do not stay the same forever but neither do they change daily.

Norms rise, govern behavior, fall out of favor, and are replaced. This pattern is evident across all levels of politics. Political ideology within the United States, for instance, swings between liberal and conservative—not on a day-to-day basis, but rather on a decade-to-decade basis. The relations between the superpowers in the cold war went through identifiable phases as well. What this model, designed to analyze norm emergence and evolution, potentially captures is a general dynamic whereby interdependent agents can reach intersubjective agreement—there exist stable expectations or a stable pattern of behavior—and those agents can alter that agreement—change is inevitable as well. Finnemore and Sikkink have captured this dynamic in the norm life cycle. The results of the model support their conclusion that norm entrepreneurs are a key factor in creating the dynamic by providing the input of new ideas.

The most important result of the modeling exercises is that the norm life cycle is able to produce these empirically relevant metastable patterns—the norm life cycle captures essential, recognizable patterns and is thus a strong candidate explanation for norm dynamics. The model demonstrates that the constructivist expectations for norm entrepreneurs are indeed plausible. Norm entrepreneurs are able to engender the rise of norms in a population of agents that cannot reach them through uncoordinated behavior and they can catalyze change in the existing normative structure as well. Importantly, this metastable pattern only arises in the model when norm entrepreneurs are present, and only at levels of social complexity where the noise (10 percent) would drive an entrepreneurless system into volatile patterns. Norm entrepreneurs alter the system dynamics and are required to produce patterns we would recognize as norm emergence and change. Thus, this model, designed with constructivist insights as its foundation, strengthens the theoretical claims of the con-structivist approach to norms.

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