Theodor Cimpeanu, Francisco C Santos, The Anh Han
Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is imperative to optimise spending without sacrificing positive outcomes, a challenge which has often been approached within several areas of social, life and engineering sciences. These studies often neglect the availability of information, cost restraints, or the underlying complex network structures, which define real-world populations. Here, we have extended these models, including the aforementioned concerns, but also tested the robustness of their findings to stochastic social learning paradigms. Akin to real-world decisions on how best to distribute endowments, we study several incentive schemes, which consider information about the overall population, local neighbourhoods, or the level of influence which a cooperative node has in the network, selectively rewarding cooperative behaviour if certain criteria are met. Following a transition towards a more realistic network setting and stochastic behavioural update rule, we found that carelessly promoting cooperators can often lead to their downfall in socially diverse settings. These emergent cyclic patterns not only damage cooperation, but also decimate the budgets of external investors. Our findings highlight the complexity of designing effective and cogent investment policies in socially diverse populations.

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The Nobel Laureate Giorgio Parisi is interviewed by JPhys Complexity Editor-in-Chief, Ginestra Bianconi, on themes related to the 2021 Nobel Prize in Physics awarded to him for research on complex systems.

Read the full article at: iopscience.iop.org

Ginestra Bianconi et al 2023 J. Phys. Complex. 4 010201

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.

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Thomas Schmickl

Complexity Volume 2022 | Article ID 9956885
Predictions of emergent phenomena, appearing on the macroscopic layer of a complex system, can fail if they are made by a microscopic model. This study demonstrates and analyses this claim on a well-known complex system, Conway’s Game of Life. Straightforward macroscopic mean-field models are easily capable of predicting such emergent properties after they have been fitted to simulation data in an after-the-fact way. Thus, these predictions are macro-to-macro only. However, a micro-to-macro model significantly fails to predict correctly, as does the obvious mesoscopic modeling approach. This suggests that some macroscopic system properties in a complex dynamic system should be interpreted as examples of phenomena (properties) arising from “strong emergence,” due to the lack of ability to build a consistent micro-to-macro model, that could explain these phenomena in a before-the-fact way. The root cause for this inability to predict this in a micro-to-macro way is identified as the pattern formation process, a phenomenon that is usually classified as being of “weak emergence.” Ultimately, this suggests that it may be in principle impossible to discriminate between such distinct categories of “weak” and “strong” emergence, as phenomena of both types can be part of the very same feedback loop that mainly governs the system’s dynamics.

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Ric Sims  and Özlem Yilmaz

Adaptive Behavior

The tricky question in the plant cognition debate is what theory of cognition should be used to fix the reference of cognitive concepts without skewing the debate too much one way or the other. After all, plants are rather different to animals in many respects: they are not motile, do not possess central nervous systems or even neurons, do not exhibit an invariant morphology, interact with the world in a distributed multi-centred manner, and behave through changes in their physiology. Nonetheless, there is a significant strand in the debate that asserts that plants are indeed cognitive. But what theory of cognition makes sense of this claim without baking in prior zoological assumptions? The aim of this paper is to try out a theory of minimal cognition that makes the claim of plant cognition plausible. It is primarily inspired by the distributed cognition literature and the sensorimotor coordination theory of cognition proposed by van Duijn et al. (2006). We take a cognitive system to be a coordinated set of semi-autonomous processes running over the organism and items in its environment. Coordination is characterised in terms of two functional conditions that ensure that the system generates goal-directed action in the world. The system is stigmergic in the sense that the material results of its actions in the environment are a crucial part of the processes that coordinate further actions. The account possesses a degree of scale invariance and helps unify cognitive explanation across microorganisms, plants and animals.

Read the full article at: journals.sagepub.com