Using insights from cybernetics and an information-based understanding of biological systems, a precise, scientifically inspired, definition of free-will is offered and the essential requirements for an agent to possess it in principle are set out. These are: a) there must be a self to self-determine; b) there must be a non-zero probability of more than one option being enacted; c) there must be an internal means of choosing among options (which is not merely random, since randomness is not a choice). For (a) to be fulfilled, the agent of self-determination must be organisationally closed (a `Kantian whole’). For (c) to be fulfilled: d) options must be generated from an internal model of the self which can calculate future states contingent on possible responses; e) choosing among these options requires their evaluation using an internally generated goal defined on an objective function representing the overall `master function’ of the agent and f) for `deep free-will’, at least two nested levels of choice and goal (d-e) must be enacted by the agent. The agent must also be able to enact its choice in physical reality. The only systems known to meet all these criteria are living organisms, not just humans, but a wide range of organisms. The main impediment to free-will in present-day artificial robots, is their lack of being a Kantian whole. Consciousness does not seem to be a requirement and the minimum complexity for a free-will system may be quite low and include relatively simple life-forms that are at least able to learn.

 

Farnsworth, K. Can A Robot Have Free Will?. Preprints 2017, 2017020105 (doi: 10.20944/preprints201702.0105.v1).

Source: www.preprints.org

Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.

 

Adaptive Local Information Transfer in Random Boolean Networks

Taichi Haruna

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 105-118
Posted Online February 27, 2017.
(doi:10.1162/ARTL_a_00224)

Source: www.mitpressjournals.org

Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of interorganizational networks led to the increase of Japanese automakers’ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.

 

Analyzing the coevolution of interorganizational networks and organizational performance: Automakers’ production networks in Japan

Matous, P. & Todo, Y. Appl Netw Sci (2017) 2: 5. doi:10.1007/s41109-017-0024-5

Source: link.springer.com

•The usefulness of quantitative indicators of ecological complexity is evaluated.
•Chaos should not be confused with complexity.
•Light and temperature cause different ranges of complexity in the gradient.
•Homoeostasis variation is related to the seasonal changes and transitions.
•Autopoiesis reveals groups with higher and lower degree of autonomy.

 

Complexity of lakes in a latitudinal gradient

Nelson Fernández, José Aguilar, C.A. Piña-García, Carlos Gershenson

Ecological Complexity
Volume 31, September 2017, Pages 1–20

Source: www.sciencedirect.com

In today’s polarized political environment, public discussion about refugees and migrants has become heated and muddled. Nature examines the facts around migration and the increasing use of technology to monitor people’s mobility. And we talk to scientists about their experiences and concerns when moving between, and living and working in, other countries.

Source: www.nature.com