Complex Insight - Understanding our world

Social systems are among the most complex known. This poses particular problems for those who wish to understand them. The complexity often makes analytic approaches infeasible and natural language approaches inadequate for relating intricate cause and effect. However, individual- and agent-based computational approaches hold out the possibility of new and deeper understanding of such systems.

 Simulating Social Complexity examines all aspects of using agent- or individual-based simulation. This approach represents systems as individual elements having each their own set of differing states and internal processes. The interactions between elements in the simulation represent interactions in the target systems. What makes these elements "social" is that they are usefully interpretable as interacting elements of an observed society. In this, the focus is on human society, but can be extended to include social animals or artificial agents where such work enhances our understanding of human society.

This handbook is intended to help in the process of maturation of this new field. It brings together, through the collaborative effort of many leading researchers, summaries of the best thinking and practice in this area and constitutes a reference point for standards against which future methodological advances are judged.

My preferred job title is 'theorist', but that is often too ambiguous in casual and non-academic conversation, so I often settle for 'computer scientist'. Unfortunately, it seems that the overwhelming majority of people equate ...

ECAL 2013, the twelfth European Conference on Artificial Life, presents the current state of the art of a mature and autonomous discipline collocated at the intersection of a theoretical perspective (the scientific explanations of different levels of life organizations, e.g., molecules, compartments, cells, tissues, organs, organisms, societies, collective and social phenomena) and advanced technological applications (bio-inspired algorithms and techniques to building-up concrete solutions such as in robotics, data analysis, search engines, gaming).

Advances in Artificial Life, ECAL 2013

Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems

Edited by Pietro Liò, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone

http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013

GE Healthcare is pushing a system called Corvix for doing agent-based simulations on complex problems. In India, the technology simulated a population of 80 million people in order to determine the best places to build medical facilities.

Around the world, the health care system is rife with inefficiencies, and General Electric thinks it can help solve the problem using data. Only it’s not talking about bureaucrats looking at reports: GE has built an artificial intelligence system called Corvix that uses historical data to predict the future, including everything from how diseases will spread to the cities where hospitals will be needed the most.

It might sound futuristic, but the techniques behind Corvix have actually been around for a while. The platform uses agent-based modeling to build, essentially, a reasonable facsimile of some sort of complex system and then simulate its evolution over time. The “agents” represent the atomic units of those systems, such as individual people in the case of human populations or perhaps cells in the case of a biological simulation. They act according to a set of rules in any given situation, which is how the models are able to keep the simulations progressing.

However, thanks to the advent of big data, GE Healthcare Chief Economist Mitch Higashi thinks the time is right for a platform like Corvix to provide some real value to real-world decisions. There’s enough raw computing power, machine intelligence and data-modeling expertise to start doing fast, accurate simulations over very large and complicated datasets. Also, advances in user-interface design have made these types of models more consumable: GE’s Corvix uses a game-like UI “that any 10-year-old can figure out how to use in 10 minutes,” Higashi said.

The first live run for Corvix happened in the state of Andhra Pradesh in India, where the system simulated a population of 80 million people in order to figure out where to build hospitals and medical training centers over the coming years. The GE team used two census datasets and one health care survey in order to build what Higashi calls “a reasonable representation of 80 million people,” as well as a map of India’s existing hospital and energy grid. Health care analysts studying the problem of where to build can drag a new hospital over an area on the map and see how the situation plays out, Higashi explained.

The original plan, said Chaitanya Sarawate, GE’s head of health economics and reimbursement for India, was for the Public Health Foundation of India to invest $2 billion building training institutions in different cities over the next five years. Corvix suggested some possible changes in location of those institutions, including placing two institutions in the country’s most-populous state, Uttar Pradesh, instead of just one as was originally planned. The advice is part of a report from the foundation to India’s Minstry of Health, which will make the ultimate decision.

Developing countries such as India are great places to use this type of technology, Higashi explained, because they are doing greenfield investing in areas such as health infrastructure and a lot of good can happen if they get it right off the bat. The problem, Sarawate noted, is that they often lack detailed data that can help governments make objective comparisons — that’s the kind of stuff a company like GE, in this case, can track down and try to feed into a model that takes into account its relative importance.

Accessing information efficiently is vital for animals to make the optimal decisions, and it is particularly important when they are facing predators. Yet until now, very few quantitative conclusions have been drawn about the information dynamics in the interaction between animals due to the lack of appropriate theoretic measures. Here, we employ transfer entropy (TE), a new information-theoretic and model-free measure, to explore the information dynamics in the interaction between a predator and a prey fish. We conduct experiments in which a predator and a prey fish are confined in separate parts of an arena, but can communicate with each other visually and tactilely. TE is calculated on the pair’s coarse-grained state of the trajectories. We find that the prey’s TE is generally significantly bigger than the predator’s during trials, which indicates that the dominant information is transmitted from predator to prey. We then demonstrate that the direction of information flow is irrelevant to the parameters used in the coarse-grained procedures. We further calculate the prey’s TE at different distances between it and the predator. The resulted figure shows that there is a high plateau in the mid-range of the distance and that drops quickly at both the near and the far ends. This result reflects that there is a sensitive space zone where the prey is highly vigilant of the predator’s position.

Information Dynamics in the Interaction between a Prey and a Predator Fish
Feng Hu, Li-Juan Nie and Shi-Jian Fu

Entropy 2015, 17(10), 7230-7241; http://dx.doi.org/10.3390/e17107230 ;

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

Batool K, Niazi MA (2014) Towards a Methodology for Validation of Centrality Measures in Complex Networks. PLoS ONE 9(4): e90283. http://dx.doi.org/10.1371/journal.pone.0090283

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The result is now clear to just about everyone on the planet.  The smartest guys in the room were no match for terabytes of data and smart algorithms.  There is no one “theory of the case” anymore, but thousands of them, being run constantly.  The point isn’t to be right, but to become less wrong over time.

The school is intended for postdocs, lecturers and predocs with a background in computer science (artificial intelligence) or computational linguistics (corpus linguistics or natural language processing) and a strong interest in music and the origins of language. There will be background lectures that introduce concepts from biology, anthropology, psychology, music theory and linguistics that are helpful to understand the nature of creativity, the role and intimate relations between language and music, and the mechanisms underlying cultural evolution. It contains technical lectures on the fundamental computational components required for language processing and technical ateliers to learn how to set up evolutionary linguistics experiments. Participants have the opportunity to present their latest research in a poster session. The school also features artistic ateliers in which participants create new creative works and engage in performance.

Music and the Origins of Language
International Summer School on Agent-based Computational Models of Creativity, 15 – 20 September 2013 in Cortona (Italy)

http://ai.vub.ac.be/events/cortona-2013