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The problem with algorithms: magnifying misbehaviour

The problem with algorithms: magnifying misbehaviour | CS_Math | Scoop.it

Computers that learn from and repeat human behaviour save time and money, but what happens when they repeat flawed traits or errors thousands of times per second?

 

By the time you read these words, much of what has appeared on the screen of whatever device you are using has been dictated by a series of conditional instructions laid down in lines of code, whose weightings and outputs are dependent on your behaviour or characteristics.

 

We live in the Age of the Algorithm, where computer models save time, money and lives. Gone are the days when labyrinthine formulae were the exclusive domain of finance and the sciences - nonprofit organisations, sports teams and the emergency services are now among their beneficiaries. Even romance is no longer a statistics-free zone.

 

But the very feature that makes algorithms so valuable - their ability to replicate human decision-making in a fraction of the time - can be a double-edged sword. If the observed human behaviours that dictate how an algorithm transforms input into output are flawed, we risk setting in motion a vicious circle when we hand over responsibility to The Machine.


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Short Course on Complexity: Exploring Complex Networks

Short Course on Complexity: Exploring Complex Networks | CS_Math | Scoop.it

September 4-6, 2013
Austin, Texas

http://www.santafe.edu/education/schools/short-course-complexity/

 

This two-and-a-half day introductory course focuses on the science of networks: a new field that studies common principles of complex networks across disciplines. Social and economic networks, food webs, the World Wide Web, and the power grid are examples of the kinds of systems that network science seeks to understand. In this course, taught by prominent Santa Fe Institute faculty and associates, you will learn the basic concepts and tools of this new science, and see several case studies of their application in diverse areas. You will also have the opportunity for discussion with the faculty and other participants about applications within your own areas of interest. You will come away with an understanding and appreciation of the importance of network science for biology, ecology, economics, business, human health, social life, and other pursuits.


Via Complexity Digest, Ashish Umre
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Lesson from Iraq: It Takes a Network to Defeat a Network

Lesson from Iraq: It Takes a Network to Defeat a Network | CS_Math | Scoop.it

“This is what we look like,” I said, as I drew a line-and-block chart on a whiteboard inside our Balad, Iraq headquarters. It laid out a neat, geometric hierarchy. “So”—I pointed to the same diagram—“this what we’re looking for.”

 

I was referring to the overarching strategy of my Special Operations Task Force and the individual inclinations of the operators that comprised it: We desperately wanted Al Qaeda in Iraq to be organized like we were, so that we could understand it, analyze it, pick it apart, and, ultimately, defeat it. Remove the leadership, some believed, and the organization would crumble.

 

“But I think we’re in agreement that what we’re actually facing looks more like this,” I said, taking the marker and drawing a new structure: my pen jumped around the board, scattering blue circles randomly across the white surface. Between them, I added seemingly arbitrary connections. One circle linked to five others, one of those five connected to three more, and so on. Unlike the comforting, symmetric right angles of the hierarchy, the lines between these nodes were erratic, varying in length, direction, and logic. I had drawn a network, and the randomness of my sketch implied the complex social, familial, tribal, and marital ties that connected our enemy, Al Qaeda. On that day, the actual arrangement of those links remained opaque to us—the dots and lines on the board simply represented an abstraction of what we believed we were facing.

 


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Characteristic exponents of complex networks

We propose a method to characterize and classify complex networks based on the time series generated by random walks and different node properties. The analysis of the fluctuations of the time series reveals the presence of long-range correlations, and allows to define, for each network, a set of characteristic exponents that capture its essential structural properties. By considering a large data set of real-world networks, we show that the characteristic exponents can be used to classify complex networks according to their function, and are able to discriminate social from biological and technological systems.

  


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Second Order Swarm Intelligence

An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial Optimization Problems, running on symmetrical TSP's. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Gruter [28] where "No entry" signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.


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Cartels Are an Emergent Phenomenon, Say Complexity Theorists

Under certain market conditions, cartels arise naturally without collusion. This raises important questions over how the behavior should be controlled.

 

The price of gas is a puzzle. Monitor the average price in gas stations in a particular city and it will vary dramatically, sometimes in a matter of hours and often in ways that appear cyclical. 

 

Economist have long scratched their heads over this kind of pattern. One explanation is that this behaviour emerges when two competing companies change their pricing strategy at each stage by reacting to the other. The resulting behaviours are known as Edgeworth Price Cycles.

 

The problem is that gas station prices are not controlled by two competing players but many competing retailers. It’s easy to assume that the many-body problem produces similar patterns but nobody has been able to show this. 

 

Until now. Today Tiago Peixoto and Stefan Bornholdt, physicists at the University of Bremen in Germany, show how a more complicated model with many buyers and sellers reproduces this kind of behaviour. 

 

But it also goes further. Peixoto and Bornholdt say that when condition are right, cartel-like behaviour emerges naturally without collusion between sellers.


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Antifragility: Learn to thrive in a volatile and complex world by creating "antifragile" organizations that thrive on stress and disorder

Antifragility: Learn to thrive in a volatile and complex world by creating "antifragile" organizations that thrive on stress and disorder | CS_Math | Scoop.it

Learn to thrive in a volatile and complex world by creating "antifragile" organizations that thrive on stress and disorder

A two-day program for senior management

November 4th and 5th, 2013
Cambridge, MA 

Speakers: 

Nassim Nicholas Taleb, Distinguished Professor, Polytechnic Institute of New York University

 

Yaneer Bar-Yam, President and Professor, 
New England Complex Systems Institute

Frame:

When strong winds blow, don't build walls, but rather windmills: there is a way to turn every bit of adversity into fuel for improvement.

This course introduces the principles of antifragility and complex systems science to explain how organizations and markets respond to volatility. Participants will learn which organizations can be considered fragile or antifragile, why certain patterns and trends matter while others are just noise, and how to create organizations that use volatility, variability, stress and disorder as information for making better decisions.


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