In this episode, Haley interviews research professor and leader of the Self-Organizing Systems Lab at UNAM, Carlos Gershenson. Gershenson discusses findings from his book, Complexity: 5 Questions, which is comprised of “interview style contributions by leading figures in the field of complexity”. He also shares his own perspectives on the past, present and future of complexity science, as well as how philosophy plays a role in the emergence of science.
How could your local library best serve your family’s and community’s needs?
We have in our society two types of publicly supported institutions whose explicit purpose is education—schools and libraries. How different they are from one another! The primary difference is this: Schools are places of forced education (or forced attempts at education) and libraries are places of voluntary, self-chosen education. The other differences follow from that. Schools try to mold us through a predetermined curriculum; libraries try to serve us by responding to our own wishes.
“Helicopter parenting” — or, the practice of hovering over your kids and watching everything they do, constantly worrying about whether they’ll make a slight misstep in life — is everywhere. I’m writing this at a coffee shop and if I so much as raise my head I can see an easy half-dozen examples of parents perhaps too invested in every move their child makes.
Posted in Parenting
Why be humble? After all, Aristotle said: ‘All men by nature desire to know.’ Intellectual humility is a particular instance of humility, since you can be down-to-earth about most things and still ignore your mental limitations. Intellectual humility means recognising that we don’t know everything – and what we do know, we shouldn’t use to our advantage. Instead, we should acknowledge that we’re probably biased in our belief about just how much we understand, and seek out the sources of wisdom that we lack.
Posted in Humility
Report from an NSF Workshop in May 2017. This workshop brought together a diverse group of experts in complementary areas of complex systems and was preceded by a series of weekly webinars(see references W1 to W6). The overarching goal of the activity was to address scientific issues that are relevant to the research community and reveal possible areas of opportunity for multidisciplinary research in the study of complex systems. The specific goals of the workshop included: o identifying the most substantive research questions that can be addressed by fundamental complex systems research; o recognizing community needs, knowledge gaps, and barriers to research progress in this area; o identifying future directions that cut across disciplinary boundaries and that are likely to lead to transformative multidisciplinary research in complex systems. The workshop was held at the National Science Foundation on May 1-3, 2017, supported by Award # DMS-1647351, and was attended by 48 scientists, mathematicians, and engineers.
Courses are often required, for example, for legal certification. But sometimes you can avoid courses, which are often costly, inconvenient, and larded with masses of material you’ll never use or have forgotten or become obsolete by the time you need it. For many people and for many things you’ll want to learn, a wonderful way to learn is be an autodidact, teaching yourself. It may be best explained by examples. I’ll offer three from my life.
According to Andy Clark “[M]uch of what goes on in the complex world of humans, may thus, somewhat surprisingly, be understood in terms of so-called stigmergic
algorithms” (Clark, 1996, p. 279; 1997, p. 186). Pierre-Paul Grasse´, the brilliant mind who first conceptualized the notion probably would not disagree (Grasse´, 1959). Grasse´ was as much a zoologist as he was an entomologist. Under his editorship the monumental (17-volume) Traite´ de Zoologie, Anatomie, Syste´matique, Biologie was guided.1 Arguably one of the most ambitious and audacious publishing endeavors ever undertaken in a science (Wing, 1950), it has come to be known affectionately as le Grasse´. It is with the recognition of this fact that Grasse´ would perhaps be gratified, if not surprised, that the term “stigmergy” has achieved such wide currency and that he might well agree that perhaps.
Posted in Stigmergy
The emergence of groups and of inequality is often traced to pre-existing differences, exclusionary practices, or resource accumulation processes, but can the emergence of groups and their differential success simply be a feature of the behaviors of a priori equally-capable actors who have mutually adapted? Using a simple model of behavioral co-adaptation among agents whose individual actions construct a common environment, we present evidence that the formation of unequal groups is endemic to co-adaptive processes that endogenously alter the environment; agents tend to separate into two groups, one whose members stop adapting earliest (the in-group), and another comprising agent who continue to adapt (the out-group). Over a wide range of model parameters, members of the in-group are rewarded more on average than members of the out-group. The primary reason is that the in-group is able to have a more profound influence on the environment and mold it to the benefit of its members. This molding capacity proves more beneficial than the persistence of adaptivity, yet, crucially, which agents are able to form a coalition to successfully exert this control is strongly contingent on random aspects of the set of agent behaviors. In this paper, we present the model, relevant definitions, and results. We then discuss its implications for the study of complex adaptive systems generally.
This paper proposes a model and theory of leadership emergence whereby (1) small social groups are modeled as small world networks and a betweeness metric is shown to be a property of networks with strong leadership, and (2) a theory of group formation based on stigmergy explains how such networks evolve and form. Specifically, dominant actors are observed to emerge from simulations of artificial termites constructing a wood chip network in a random walk, suggesting a correlation between various preferential attachment rules and emergent network topologies. Three attachment rules are studied: maximizing node betweeness (intermediary power), maximizing node degree (node connectivity), and limiting radius (size of the network in terms of network distance). The simulation results suggest that a preference for maximizing betweeness produces networks with structure similar to the 62-node 9-11 terrorist network. Further simulations of emergent networks with small world properties (small radius) and high betweeness centrality (strong leader) are shown to match the topological structure of the 9-11 terrorist network, also. Interestingly, the same properties are not found in a small sampling of human made physical infrastructure networks such as power grids, transportation systems, water and pipeline networks, suggesting a difference between social network emergence and physical infrastructure emergence. Additionally, a contagion model is applied to random and structured networks to understand the dynamics of anti-leader sentiment (uprisings and counter-movements that challenge the status quo). For random networks, simulated pro-leader (pro-government) and anti-leader (pro-rebel) sentiments are propagated throughout a social network like opposing diseases to determine which sentiment eventually prevails. Simulations of the rise of rebel sentiment versus the ratio of rebel to government sentiment show that rebel sentiment rises on less than 100% rebel/government sentiment when government sentiment is high (strong leadership), but requires greater than 100% rebel/government sentiment when government sentiment is low (weak leadership). However, when applied to the structured 9-11 terrorist network, rebel sentiment is slow to rise against strong leadership, because of the high betweeness structure of the 9-11 network. These results suggest a theory of how and why human stigmergy evolves networks with strong leaders, and why successful social networks are resilient against anti-leader sentiment. The author concludes that a combination of small world and high betweeness structure explain how social networks emerge strong leadership structure and why the resulting networks are resilient against being overthrown by a dissenting majority.
Read also: Human stigmergy: Theoretical developments and new applications