Computer Software to Predict the Unpredictable 287
Amigan writes "Professor Jerzy Rozenblit at the University of Arizona was awarded $2.2Million to develop software to predict the unpredictable — specifically relating to volatile political and military situations." From the article:
"The software will predict the actions of paramilitary groups, ethnic factions, terrorists and criminal groups, while aiding commanders in devising strategies for stabilizing areas before, during and after conflicts.
It also will have many civilian applications in finance, law enforcement, epidemiology and the aftermath of natural disasters, such as hurricane Katrina."
Read "Minority Report" By P.K. Dick. (Score:5, Informative)
Precog #1 sees the future.
Precog #2 sees the future that happens when you know what Precog #1 saw.
Precog #3 sees the future that happens when you know what Precog #2 saw which was the future that Precog #1 saw.
Re:computer? (Score:5, Informative)
Readers of Asimov will know the qualitative reasons for why such things as broad socio-economic-historical trends and the actions of large groups of people can in principle be made predictable. For a system to be chaotic, it must have a large PHASE SPACE of possibilities (physical size is not always important but it is significant). What matters is the degrees of freedom and how parts of the system are coupled to other parts. Do small perturbations in the system dissipate or do they spread? Modern society has evolved into a 2-phase system where it reacts to new perturbations by simply breaking them into two possibilities - this helps relieve tensions and most people get stuck in one of the two states. This has the rather fascinating effect of re-stabilizing the system despite the introduced disturbance.
So, as the above example leads us to suspect, modern human societies are just not as complex as our egos would lead us to believe. There is strong coupling between its parts and few people stay undecided about issues - they simply get stuck orbiting one of two strong attractors in the space of possibilities and this serves to relieve any stress. In such a system of course, revolutions (in the sense of widely held beliefs changing within the lifetime of a single individual) simply cannot happen. At the worst, there might be a slow decay and unraveling of the social fabric. Barely noticeable.
Equivalent arguments apply to the "free will" of individual human beings. Humans tend to congregate in packs - behaviorally, philosophically or otherwise. This strong tribal leaning that is presumably built into our genes ensures that most behavior patterns will be statistical in nature. Indeed, the actions of an individual can be simply predicted to a first approximation by merely qualitative means even in the absence of complete information by assuming rational behavior. A better approximation can be achieved by modeling the level of rationality of the individual and assigning probabilities based on that.
While human beings may not be predictable in a strictly deductive sense, most people are (for better or for worse) rather mundane in terms of how eccentric they can be (in a way that actually affects other parts of society). This can hardly be a bad thing as the timescale of societal change must be greater than the lifetime of an individual for a society to be called "stable". If it is MUCH greater, we would call that society degenerate or decayed.
Rozenblit's work on coevolution/genetic algorithms (Score:5, Informative)
A coevolutionary approach to course of action generation and visualization in multi-sided conflicts [ieee.org]
The current state of military operations includes many stability and support (SASO), multi-sided conflicts. The research presented in this paper attempts to address this complex environment by creating a SASO simulation, coevolutionary generation of courses-of-actions (COAs) for each side, and visualization tools for analysis of the resulting COAs. The SASO simulation is significantly different from previous systems because it incorporates non-conventional warfare units such as terrorists and media. The coevolution algorithm is different because it allows all sides of the conflict to evolve their COAs. The visualization tools are important because SASO doctrine is not as well developed as conventional warfare doctrine. Therefore, visual analysis and understanding of a system that is not well defined provides insight for future modeling and verification.
Modeling and simulation of stability and support operations (SASO) [ieee.org]
Stability and support operations (SASO) are becoming increasingly important in modern military operations. Conflicts are no longer comprised solely of two opposing sides engaged in combat on an open battlefield. Instead, they are more likely to involve groups sharing various alliances and relationships each pursuing a range of different goals. The Sheherazade SASO wargaming engine presented here: a) incorporates subjective criteria for scoring course of action (COA) success such as the animosity between factions and attitudes of locales, b) uses nontraditional units such as refugees, media and information operators, and c) employs a coevolutionary genetic algorithm in modeling the dynamics of the complex multisided simulation for generating COAs. This paper outlines our approach towards the development of a wargaming model that handles the more complex and computationally demanding arena of SASO.