Propagation of Innovations 1 Running head: PROPAGATION OF INNOVATIONS IN NETWORKS Propagation of Innovations in Networked Groups Winter Mason Indiana University

A novel paradigm was developed to study the behavior of groups of networked people searching a problem space. We examined how different network structures affect the propagation of information in laboratory-created groups. Participants made numerical guesses and received scores that were also made available to their neighbors in the network. The networks were compared on speed of discovery and convergence on the optimal solution. One experiment showed that individuals within a group tend to converge on similar solutions even when there is an equally valid alternate solution. Two additional studies demonstrated that the optimal network structure depends on the problem space being explored, with networks that incorporate spatially-based cliques having an advantage for problems that benefit from broad exploration, and networks with greater long-range connectivity having an advantage for problems requiring less exploration. Propagation of Innovations 3 Diffusion of Information and Innovations Humans are uniquely adept at adopting one others’ innovations. While imitation is commonly thought to be the last resort for dull and dim-witted individuals, cases of true imitation are rare among non-human animals (Blackmore, 1999), requiring complex cognitive processes of perception, analogical reasoning, and action preparation. This is because true imitation requires understanding the intentions of the model, rather than simply the behaviors. Gergely, Bekkering, and Király (2002), replicating a study by Melztoff (1988), showed that infants can infer the intentions of an adult model rather than simply copying the behavior. This capacity for imitation has been termed “no-trial learning” by Bandura (1965), who stressed that, by imitating one another, people perform behaviors that they would not have otherwise considered. When combined with variation and adaptation based on reinforcement, imitation is one of the most powerful methods for quick and effective learning. Cultural identity is largely due to the dissemination of concepts, beliefs, and artifacts across people. The tendency for humans to imitate is so ubiquitous that Meltzoff (1988) has even suggested that humans be called “Homo imitans. ” This tendency is also evident in conformity in social groups. One famous experiment on conformity is Asch’s (1956) line judgment experiment, in which a series of confederates announced that a pair of lines was the same length, even though they differed by several inches. When the participants had to make their judgment, roughly one-third conformed to the group and declared the same pair of lines to be equal. There are several reasons why the participants may have conformed to the objectively incorrect judgment. Influences can be classified into two types (Deutsch & Gerard, 1955): normative influence, when people are influenced because they desire to obtain social approval from others, and informational influences, when people are influenced because they believe others possess additional or more accurate information. Cialdini and Goldstein (2001) note that having an affiliation goal, making one susceptible to normative influence, and having an accuracy goal, making one susceptible to informational influence, are both subsumed by a general goal of maintaining one’s self-concept. The Propagation of Innovations 4 different kinds of influence affect the stability of the influence. With normative influence, people are less likely to conform to the group’s opinion when responding privately than when responding publicly (Deutsch & Gerard, 1955). However, when the influence is informational (e.g., Sherif, 1935), people are more likely to continue to conform to the group’s opinion privately. At a broader level, the “I’ll do it if you do it” mentality can lead to situations in which a small number of people can initiate a positive feedback cycle, an effect that has been popularized as the notion of “tipping points,” (Gladwell, 2000). This effect is viscerally apparent if you have ever witnessed a single person start a chant at a stadium that catches on until everyone in the stadium is participating in the chant. The spread of a chant in a stadium, though operating through a process of conformity, behaves in a manner very similar to disease contagion. In fact, the spread of affect in a population has been described as “mood contagion,” (Neumann & Strack, 2000), and the spread of attitudes and beliefs through a population has been modeled as a similar process. Nowak, Szamrej, and Latané (1990) used a computational model to study the dynamic impact of social influence. Friedkin and Johnsen (1999) developed a comparable mathematical model of influence with two components, the social process and the social influence structure. The first is the way in which people modify their opinions in response to influence, and the second is the structure of communications channels and the strength of influence between people in a group. These influence models provide a more specific and formal description of conformity processes within a group. Laughlin and Ellis (1986) describe a continuum between intellective and judgmental issues. Where an issue falls on this continuum depends on the demonstrability of the correctness of solutions. A math problem, which has accepted means for showing the correctness of a solution would be considered an intellective task, whereas which flavor of ice cream tastes better would be judgmental. However, many problems fall in between this continuum, such as who to hire as a job candidate. While there are objective and accepted means of showing the advantage of one candidate over another on Propagation of Innovations 5 certain dimensions, such as the number of publications, there can be differences of opinion as to the quality of the publications or even which dimensions are most important for hiring a candidate. Kaplan and Miller (1987) looked at the difference in conformity for intellective and judgmental issues with normative and informational influence. Their somewhat intuitive conclusion was that intellective issues led to more informational influence while judgmental issues led to more normative influence. The dissemination of innovations in a population is also the result of social influence and conformity and so can manifest the same bandwagon behavior. An innovation may spread because it is clearly better than the alternatives, so the conformity is due to the informational influence of others. Innovations may also function more like opinions when it is more difficult to determine if the innovation is better than current practice, or in other words, when the advantage of the innovation is less demonstrable and therefore is less of an intellective issue. If there is no inherent difference, or when the benefits of adopting an innovation are largely due to others using it (e.g., Macintosh vs. IBM or BetaMax vs. VHS), the innovation is even more like a judgmental issue. So innovations can spread in the same manner as crowd chants, affect, and attitudes. This spread of innovations has been studied from many different perspectives beginning with Gabriel Tarde (1903), a sociologist who noted that the number of people adopting an innovation over time produces a sigmoidal curve for many different kinds of innovation, with slow adoption at first, then a sudden increase that levels off once the innovation has almost fully saturated the population. Ryan and Gross (1943) studied the adoption of hybrid-corn use by Iowa farmers, focusing on individual differences in time of adoption. Bass (1969) borrowed differential equations from physics to model the populationlevel change in the number of people choosing to use an innovation, and reproduced the S-shaped curve suggested by Tarde. This diffusion model is so popular that it is commonly referred to as the “Bass model,” and the term “diffusion of innovation” has become ubiquitous in the field. For a good review of Propagation of Innovations 6 the literataure on the adoption of innovations, see Rogers (1962, 1995), and more recently, Wejnert (2002). The choice between relying on information from others and obtaining information on one’s own involves a tradeoff of costs and benefits. Seeking out information on one’s own requires time and energy, but is often more trustworthy and individually tailored than information learned by word-ofmouth. On the other hand, choosing to use information provided by others can be cost-effective, especially if past experience suggests that the source is reliable. These two choices have been characterized as exploration and exploitation (Holland, 1975), and March (1991) presented a detailed analysis of the tradeoff between the two with respect to organizations. In those cases, organizations that rely mostly on exploitation of competitor’s innovations benefit in the short run by saving costs on research and development, but lose out in the long run because they never lead the pack. Granovetter (1978) suggested that people act as though they have a threshold number of friends (or neighbors) that must adopt a solution before they will also adopt the solution themselves, and found that the people who were early in adopting a solution (those with a low threshold) were most influential in causing bandwagoning in a population. Michael Chwe (1999) extended this threshold model and found that the network position of an individual could be more important than their threshold with respect to causing other people to follow the crowd. Valente and Davis (1999) also noted that opinion leaders, those individuals who are most central in a communication network, are the most influential in starting a bandwagoning process. This highlights the importance of another factor in the diffusion of information and innovations -the social network structure. Social Network Analysis The properties of network topologies have been studied in many different arenas, including neural networks, ac