Eugene F. Fama, Robert J. Shiller and Lars Peter Hansen win the 2013 Nobel Prize in Economic Sciences. “The Laureates have laid the foundation for the current understanding of asset prices. It relies in part on fluctuations in risk and risk attitudes, and in part on behavioral biases and market frictions.” stated the Royal Swedish Academy of Sciences.
Despite all our great advances in science, technology and financial innovations, many societies today are struggling with a financial, economic and public spending crisis, over-regulation, and mass unemployment, as well as lack of sustainability and innovation. Can we still rely on conventional economic thinking or do we need a new approach? Is our economic system undergoing a fundamental transformation? Are our theories still doing a good job with just a few exceptions, or do they work only for “good weather” but not for “market storms”? Can we fix existing theories by adapting them a bit, or do we need a fundamentally different approach? These are the kind of questions that will be addressed in this paper. I argue that, as the complexity of socio-economic systems increases, networked decision-making and bottom-up self-regulation will be more and more important features. It will be explained why, besides the “homo economicus” with strictly self-regarding preferences, natural selection has also created a “homo socialis” with other-regarding preferences.(...)
Economics 2.0: The Natural Step towards a Self-Regulating, Participatory Market Society Dirk Helbing
Evolutionary and Institutional Economics Review Vol. 10 (2013) No. 1 p. 3-41
Nowadays, any organization should employ network scientists/analysts who are able to map and analyse complex systems that are of importance to the organization (e.g. the organization itself, its activities, a country’s economic activities, transportation networks, research networks). Interconnectivity is beneficial but also brings in vulnerability: if you and I are connected we can share resources; meanwhile your problems can become mine and vice versa. The concept of “crystallized imagination” refers to things that are first in our head and then become reality. This concept can be turned into network applied research on economic complexity of a country’s economic activities and development prospects.
When a tenth of humanity lost power over 2 days in India in July 2012, technical failure was not the only culprit. Like many recent blackouts, this outage resulted from couplings among systems, including extreme weather exacerbated by climate change, human operator errors, suboptimal policies, and market forces. Predictions that climate change intensifies droughts and tropical cyclones presage more weather-induced blackouts. Even without weather disasters, small disturbances can trigger cascading failures, and so can ill-designed electricity markets (1) and dependence on cyber infrastructure.
Transdisciplinary electric power grid science Charles D. Brummitt, Paul D. H. Hines, Ian Dobson, Cristopher Moore, and Raissa M. D'Souza
There is a mounting crisis in delivering affordable healthcare in the US. For decades, key decision makers in the public and private sectors have considered cost-effectiveness in healthcare a top priority. Their actions have focused on putting a limit on fees, services, or care options. However, they have met with limited success as costs have increased rapidly while the quality isn't commensurate with the high costs. A new approach is needed. Here we provide eight scientifically-based steps for improving the healthcare system. The core of the approach is promoting the best use of resources by matching the people and organization to the tasks they are good at, and providing the right incentive structure. Harnessing costs need not mean sacrificing quality. Quality service and low costs can be achieved by making sure the right people and the right organizations deliver services. As an example, the frequent use of emergency rooms for non-emergency care demonstrates the waste of resources of highly capable individuals and facilities resulting in high costs and ineffective care. Neither free markets nor managed care guarantees the best use of resources. A different oversight system is needed to promote the right incentives. Unlike managed care, effective oversight must not interfere with the performance of care. Otherwise, cost control only makes care more cumbersome. The eight steps we propose are designed to dramatically improve the effectiveness of the healthcare system, both for those who receive services and those who provide them.
A Complex Systems Science Approach to Healthcare Costs and Quality Yaneer Bar-Yam with Shlomiya Bar-Yam, Karla Z. Bertrand, Nancy Cohen, Alexander S. Gard-Murray, Helen P. Harte, Luci Leykum
The Annual Complexity in Business Conference endeavors to be the premier meeting for the intersection of Complex Systems and Business. The 5th annual conference will be a one and a half day event and will include talks by thought leaders and an audience blend of academics and industry practitioners. We are very excited to announce that this year we will be having a concurrent track during the conference and will be accepting abstract submissions from the public. We are looking forward to a lively set of interactions among a very distinguished group of researchers and business leaders. On Thursday, November 7 at 3:00 p.m. a series of talks at the Ronald Reagan Building will kick off the conference, followed by a cocktail reception and dinner at a D.C. restaurant.
5th Annual Complexity in Business Conference Thursday and Friday, November 7 and 8, 2013 • Washington, DC
Digital technologies have made networks ubiquitous. A growing body of research is examining these networks to gain a better understanding of how firms interact with their consumers, how people interact with each other, and how current and future digital artifacts will continue to alter business and society. The increasing availability of massive networked data has led to several streams of inquiry across fields as diverse as computer science, economics, information systems, marketing, physics, and sociology. Each of these research streams asks questions that at their core involve “information in networks”—its distribution, its diffusion, its inferential value, and its influence on social and economic outcomes. We suggest a broad direction for research into social and economic networks. Our analysis describes four kinds of investigation that seem most promising. The first studies how information technologies create and reveal networks whose connections represent social and economic relationships. The second examines the content that flows through networks and its economic, social, and organizational implications. A third develops theories and methods to understand and utilize the rich predictive information contained in networked data. A final area of inquiry focuses on network dynamics and how information technology affects network evolution. We conclude by discussing several important cross-cutting issues with implications for all four research streams, which must be addressed if the ensuing research is to be both rigorous and relevant. We also describe how these directions of inquiry are interconnected: results and ideas will pollinate across them, leading to a new cumulative research tradition.
Information in Digital, Economic, and Social Networks Arun Sundararajan, Foster Provost, Gal Oestreicher-Singer and Sinan Aral
There is much enthusiasm currently about the possibilities created by new and more extensive sources of data to better understand and manage cities. Here, I explore how big data can be useful in urban planning by formalizing the planning process as a general computational problem. I show that, under general conditions, new sources of data coordinated with urban policy can be applied following fundamental principles of engineering to achieve new solutions to important age-old urban problems. I also show, that comprehensive urban planning is computationally intractable (i.e. practically impossible) in large cities, regardless of the amounts of data available. This dilemma between the need for planning and coordination and its impossibility in detail is resolved by the recognition that cities are first and foremost self-organizing social networks embedded in space and enabled by urban infrastructure and services. As such the primary role of big data in cities is to facilitate information flows and mechanisms of learning and coordination by heterogeneous individuals. However, processes of self-organization in cities, as well as of service improvement and expansion, must rely on general principles that enforce necessary conditions for cities to operate and evolve. Such ideas are the core a developing scientific theory of cities, which is itself enabled by the growing availability of quantitative data on thousands of cities worldwide, across different geographies and levels of development. These three uses of data and information technologies in cities constitute then the necessary pillars for more successful urban policy and management that encourages, and does not stifle, the fundamental role of cities as engines of development and innovation in human societies.
The Uses of Big Data in Cities Luís M. A. Bettencourt
How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today? Why is there so much variation in the ability of different human populations to construct viable states? We developed a model that uses cultural evolution mechanisms to predict where and when the largest-scale complex societies should have arisen in human history. The model was simulated within a realistic landscape of the Afroeurasian landmass, and its predictions were tested against real data. Overall, the model did an excellent job predicting empirical patterns. Our results suggest a possible explanation as to why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.
War, space, and the evolution of Old World complex societies Peter Turchin, Thomas E. Currie, Edward A. L. Turner, and Sergey Gavrilets
The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anysotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when a real-space renormalization is performed by coarse-graining the geographical scale from county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations.
Is the Voter Model a model for voters? Juan Fernández-Gracia, Krzysztof Suchecki, José J. Ramasco, Maxi San Miguel, Víctor M. Eguíluz
Scientists have derived a series of mathematical formulas that describe how cities' properties vary in relation to their population size, and then posits a novel unified, quantitative framework for understanding how cities function and grow.
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.
From New York to Istanbul, and Rio to Tunis, waves of social unrest have been sweeping across the world. Whatever they are called – Occupy Wall Street in New York, the Jasmine Revolution in Tunisia or the Arab Spring beyond, and the Salad Uprising in Brazil – the mass mobilisations share several common features. Espousing public discontent over a range of sometimes unrelated, even conflicting issues, they were driven largely by new communication technologies coupled with an abiding distrust of government policies. Unlike the formal, planned protests of earlier times, the latest ones are, for the most part, informal and relatively spontaneous. As such, scientists say, they reflect a shift away from conventional social hierarchies towards what some call leaderless networks.
How do the network positions of the first individuals in a society to receive information about a new product affect its eventual diffusion? To answer this question, we develop a model of information diffusion through a social network that discriminates between information passing (individuals must be aware of the product before they can adopt it, and they can learn from their friends) and endorsement (the decisions of informed individuals to adopt the product might be influenced by their friends’ decisions). We apply it to the diffusion of microfinance loans, in a setting where the set of potentially first-informed individuals is known. We then propose two new measures of how “central” individuals are in their social network with regard to spreading information; the centrality of the first-informed individuals in a village helps significantly in predicting eventual adoption.
The Diffusion of Microfinance Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, Matthew O. Jackson
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