Complexes

Edmonds, B. & C. Gershenson (2012).  Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test .  I nvited talk at Turing Centenary Conference CiE 2012 ,  special session on "The Turing Test and Thinking Machines".  CPM Report No.: 12-215 Abstract : The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) d...

Last November I had the honor of participating in TEDxDF with a talk on self-organizing traffic lights. You can watch the video (in Spanish) at: http://tedxtalks.ted.com/video/TEDxDF-Carlos-Gershenson-Semfor  or  http://www.youtube.com/watch?v=QohrFmeNnVw Mejorar el transporte público de la Ciudad de México es una idea que a todos se nos ocurre, pero pocos hacemos algo al respecto. Este no es el caso de Carlos, un apasionado del estudio científico de la complejidad: ¿Cómo podemos diseñar componentes de un sistema para que, por medio de sus interacciones, realicen una función deseada a nivel del sistema? Con su ponencia Carlos responderá esta pregunta y expondrá ideas aplicables al DF para mejorar diversos medios de transporte, afectando positivamente la calidad de vida de la población.

One of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations. Gershenson, C. (In Press).  Facing Complexity: Prediction vs. Adaptation . To be published in Martorell, X. & Massip, A. (Eds.) Complexity and Language . Full paper at  http://arxiv.org/abs/1112.3843 Versión en ...

This paper presents and overview of current and potential applications of living technology to urban problems. Living technology can be described as technology that exhibits the core features of living systems. These features can be useful to solve dynamic problems. In particular, urban problems concerning mobility, logistics, telecommunications, governance, safety, sustainability, and society and culture are presented, while solutions involving living technology are reviewed. Finally, the usefulness of describing cities as living systems is discussed. Gershenson, C. (2011). Living in Living Cities. C3 Report 2011.09.  http://arxiv.org/abs/1111.3659

This essay explores the limits of Turing machines concerning the modeling of minds and suggests alternatives to go beyond those limits. Gershenson, C. (2011). Are Minds Computable? C3 Tech. Report 2011.08.  http://arxiv.org/abs/1110.3002

1. Law of  Information Transformation . I nformation will potentially be transformed by interacting with other information. 2.  Law of Information Propagation .  Information propagates as fast as possible.  3.  Law of Requisite Complexity . M ore complex information will require more complex agents to perceive, act on, and propagate it. 4.  Law of Information Criticality .  Transforming and propagating information will tend to a critical balance be- tween its stability and its variability. 5.  Law of Information Organization .  Information produces constraints that regulate information production.  6.  Law of Information Self-organization .  Information tends to its preferred, most probable state.  7.  Law of Information Potentiality .  An agent can give different potential meanings to information.  8.  Law of Information Perception .  The meaning of information is unique for an agent percei...

UPDATE: Video also on youtube . You can find here the video (32 min) of a talk I gave at the last Lakeside Research Days in Klagenfurt, Austria. I speak about the results presented in this paper: Gershenson C (2011) Self-Organization Leads to Supraoptimal Performance in Public Transportation Systems. PLoS ONE 6 (6): e21469. http://dx.doi.org/10.1371/journal.pone.0021469 The main idea is that self-organization can regulate public transportation systems in such a way that passengers wait less than the theoretical optimum. Of course, this means that the theory was wrong. Well, more precisely, theory made misguided assumptions. I thank Christian Bettstetter and Wifried Elmenreich for organizing the Research Days and Chrisitan Philipp for making the video. Tip: The video is high quality, so it takes some time to download, even if one chooses right click -> Switch to low quality. Cultivate patience...