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Swarm Intelligence: Is the Group Really Smarter?

Swarm Intelligence: Is the Group Really Smarter? | Papers | Scoop.it
From peopletriggers.wordpress.com - March 15, 2012 3:02 PM
Decentralized problem solving works better on some problems than others. According to an article from SEO Theory, swarms work in situations that involve discovery, testing, and comparing results. For example, ants finding the most efficient route to food, or iPhone users who use their Yelp App to find the highest-rated restaurant close by. By leveraging the volume of agents involved, you can act upon complex and rapidly changing sets of data. This works in situations where you are looking for the most efficient method of execution, or the most optimal process. Shipping companies, for example, use Swarm Theory computer algorithms to reallocate trucking routes based on up-to-the-minute energy prices. Swarm Theory works less effectively for creative processes like innovation, except perhaps as a broad directional pointer. A Swarm cannot paint the next Mona Lisa.

Via Viktor Markowski, Fabio Vanni
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Emergent Criticality through Adaptive Information Processing in Boolean Networks

Emergent Criticality through Adaptive Information Processing in Boolean Networks | Papers | Scoop.it
From prl.aps.org - March 23, 2012 7:17 PM

We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity Kc=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near Kc. We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

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What Is Information?: Why Is It Relativistic and What Is Its Relationship to Materiality, Meaning and Organization

From www.mdpi.com - March 23, 2012 12:15 PM

We review the historic development of concept of information including the relationship of Shannon information and entropy and the criticism of Shannon information because of its lack of a connection to meaning. We review the work of Kauffman, Logan et al. that shows that Shannon information fails to describe biotic information. We introduce the notion of the relativity of information and show that the concept of information depends on the context of where and how it is being used. We examine the relationship of information to meaning and materiality within information theory, cybernetics and systems biology. We show there exists a link between information and organization in biotic systems and in the various aspects of human culture including language, technology, science, economics and governance.

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Introduction to the Special Issue on Information: Selected Papers from “FIS 2010 Beijing”

From www.mdpi.com - March 23, 2012 12:17 PM

During the last two decades, a systematic re-examination of the whole information science field has taken place around the FIS—Foundations of Information Science—initiative. With the occasion of its Fourth Conference in Beijing 2010, a group of selected contributors and leading practitioners of those fields have been invited to contribute to this Special Issue. What is the status of information science today? What is the relationship between information and the laws of nature? Is information merely “physical”? What is the difference between information and computation? Has the genomic revolution changed the contemporary views on information and life? And what about the nature of social information? Cogent answers to these questions and to quite many others are attempted in the contributions that follow.

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Experimental verification of Landauer/'s principle linking information and thermodynamics

Experimental verification of Landauer/'s principle linking information and thermodynamics | Papers | Scoop.it
From www.nature.com - March 16, 2012 7:08 PM

In 1961, Rolf Landauer argued that the erasure of information is a dissipative process. A minimal quantity of heat, proportional to the thermal energy and called the Landauer bound, is necessarily produced when a classical bit of information is deleted. A direct consequence of this logically irreversible transformation is that the entropy of the environment increases by a finite amount. Despite its fundamental importance for information theory and computer science, the erasure principle has not been verified experimentally so far (…) This result demonstrates the intimate link between information theory and thermodynamics. It further highlights the ultimate physical limit of irreversible computation.

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