Contact networks have no influence on cooperation among individuals

This is a simulation of a network of people playing Prisoner’s Dilemma. Red are cooperators; blue are defectors.

For the past twenty years, there has been a great controversy regarding whether the structure of interactions among individuals (that is, if the existence of a certain contact network or social network) helps to foment cooperation among them in situations in which not cooperating brings benefits without generating the costs of helping. Many theoretical studies have analysed this subject, but the conclusions have been contradictory since the way in which people make decisions is almost always based on a hypothesis of the models with very little basis to justify it.

The study carried out by these university researchers adopts a pioneering perspective on the theoretical study of the emergence of cooperation: rather than postulating that people make decisions according to one procedure or another, it incorporates the results obtained in experiments designed precisely to analyse how people decide whether to cooperate or not. The authors of the study are professors from the Interdisciplinary Complex Systems Group (Grupo Interdisciplinar de Sistemas Complejos – GISC) of the Mathematics Department of Carlos III University of Madrid, José Cuesta and Ángel Sánchez, together with Carlos Gracia and Yamir Moreno, from the Complex Systems and Networks Group (Grupo de Redes y Sistemas Complejos – COSNET Lab) of the Institute for Biocomputation and Physics of Complex Systems (Instituto de Biocomputación y Física de Sistemas Complejos – BIFI) of the University of Zaragoza. Their study was recently published in Scientific Reports, Nature’s new open access magazine.

This work is based on the results of an experiment carried out by the researchers and on information from other previous studies, as well as on the results (as yet unpublished) obtained from their own new experiments. The observations from these studies coincide in indicating that people do not consider what those they interact with gain; rather they think about whether or not they cooperate. In addition, their decisions usually depend on their own mood. That is, the authors noticed that the probability of cooperation occurring was considerably higher if there had been cooperation in the previous interaction. They also observed a certain heterogeneity in behaviour, finding a certain percentage of individuals who cooperated very little, regardless of what those around them did, and other individuals who almost always cooperated, again, no matter what others did.

These researchers have mathematically examined what occurs when groups of people who behave as the experiments say have to decide whether or not to cooperate, and how the existence of cooperation, globally or in the group, depends on the structure of the interactions. Specifically, the study analyses what happens if each person interacts with all of the others, if the people are placed in a square reticule and they interact with their four closest neighbours, or if they are arranged in a network that is more similar to a social network, in which the number of neighbours is highly variable and is dependent on each person. In the first case (each individual interacts with all of the others), the problem can be solved mathematically, so the level of resulting cooperation can be predicted. What the researchers observed is that this depends on the makeup of the population; that is, what proportion of the individuals use the previously described strategy, and what proportion almost always cooperates or almost never does, regardless of what the others do. Afterwards, this prediction can be compared with the results of numeric simulations obtains for the populations placed in each of the two networks, and it can be shown that the result is exactly the same, unlike what had been concluded in previous studies.

The consequences of this prediction are very important, according to the authors of the study, because if they are true, it would rule out the existence of one of the five mechanisms that have been proposed to explain the emergence of cooperation, the so-called “network reciprocity” mechanism. In order to prove the prediction, it will be necessary to carry out a large-scale experiment, something that this group of researchers in currently very involved in. These experiments are very difficult to carry out, given that studying heterogeneous networks in such a way as to obtain significant results, the team must work with hundred of volunteers simultaneously. If, as the team hopes, the experiments confirm what this study predicts, we would be witnessing a paradigm shift in the interpretation of decision-making in cooperative dilemmas: instead of considering what is to be gained, individuals would base their decisions on the cooperation they have received, and this would mean that the way that they interact (the underlying social network) would cease to be important.

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Credit of the article given to Carlos III University of Madrid


Mathematical model connects innovation and obsolescence to unify insights across diverse fields

In Lewis Carroll’s Through the Looking-Glass, the Red Queen tells Alice, “It takes all the running you can do, to keep in the same place.” The race between innovation and obsolescence is like this.

Recent evidence about the slowing of technological and scientific progress in contrast to the accelerating epidemiological risks in a globalized world—in the opposite direction—indicates the importance of the relative rates of innovation and obsolescence.

When does innovation outpace, or fail to outpace, obsolescence? Understanding this dynamic is nascent, and the way that innovation is discussed is largely fragmented across fields. Despite some qualitative efforts to bridge this gap, insights are rarely transferred.

In research led by Complexity Science Hub (CSH), Eddie Lee and colleagues have taken an important step towards building those bridges with a quantitative mathematical theory that models this dynamic.

The paper, “Idea engines: Unifying innovation & obsolescence from markets & genetic evolution to science,” is published in Proceedings of the National Academy of Sciences.

“You could say this is an exercise in translation,” says Lee, the first author of the paper. “There’s a plethora of theories on innovation and obsolescence in different fields: from economist Joseph Schumpeter’s theory of innovation, to other ideas proposed by theoretical biologist Stuart Kauffman, or philosopher of science Thomas Kuhn. Through our work, we try to open the doors to the scientific process and connect aspects of the different theories into one mathematical model,” explains Lee, a postdoc researcher at CSH.

Space of the possible, and its boundaries

Lee, together with Geoffrey West and Christopher Kempes at the Santa Fe Institute, conceives of innovation as expanding the space of the possible while obsolescence shrinks it. The “space of the possible” encompasses the set of all realized potentialities within a system.

“Within the space of the possible, you might think of different manufacturing technologies available in firms. All the living mutation species would be a good example in biology. In science, you might think of scientific theories that are feasible and empirically supported,” says Lee.

The space of the possible grows as innovations are pulled in from the “adjacent possible,” Stuart Kauffman’s term for the set of all things that lie one step away from what is possible. Lee and his co-authors compare this with an obsolescent front, which is the set of all things that are on the verge of being discarded.

Three possible scenarios

Based on this picture of the space of the possible, the team modeled a general dynamics of innovation and obsolescence to identify three possible scenarios. There is an ever-expanding scenario, where the possibilities agents are capable of growth without end. Schumpeterian dystopia is the opposite of this world, where innovation fails to outpace obsolescence. A third scenario follows the original Schumpeterian concept of creation and destruction, in which new ways of production survive by eliminating old ones.

The model was tested with real-world data from a variety of fields, from measures of firm productivity to COVID-19 mutations and scientific citations. Thus, the researchers were able to bring together examples that have heretofore been considered in isolation from one another. Both the model and the data are for the average set of dynamics rather than focusing on specific innovations, which allows for the generalization emphasized in the paper.

“We saw a remarkable similarity between all the data, from economics, biology, and science of science,” states the CSH researcher. One key discovery is that all the systems seem to live around the innovative frontier. “Moreover, agents at the boundary of innovative explosion, whether close to it or far away, share the same characteristic profile,” adds Lee, where few agents are innovative and many are near obsolescence. West likens this to systems living on the “edge of chaos,” where a small change in the dynamics can lead to a large change in the state of the system.

Universal phenomenon

The novel approach could transform our understanding of the dynamics of innovation in complex systems. By trying to capture the essence of innovation and obsolescence as a universal phenomenon, the work brings divergent viewpoints together into a unified mathematical theory. “Our framework provides a way of unifying a phenomenon that has so far been studied separately with a quantitative theory,” say the authors.

“Given the critical role that innovation in all its multiple manifestations plays in society, it’s quite surprising that our work appears to be the first attempt to develop a sort of grand unified mathematical theory which is testable to understand its dynamics,” says West. “It’s still very crude but hopefully can provide a point of departure for developing a more detailed realistic theory that can help inform policy and practitioners.”

“We provide an average model of the combined dynamics of innovation and obsolescence,” says Kempes. “In the future it is exciting and important to think about how this average model meets up with detailed theories of how innovations actually occur. For example, how do current objects or technologies get combined to form new things in something like the recently proposed Assembly Theory?”

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Credit of the article given to Complexity Science Hub