Computational Legal Studies™ » computer science

Riders on a Swarm — Might Mimicking the Behavior of Ants, Bees & Birds Be the Key to Artificial Intelligence?

dmartink — Tue, 17 Aug 2010 04:01:52 +0000

This week’s issue of the Economist has an interesting article entitled Riders on a Swarm. Among other things, the article discusses how attempts to computationally model ant, bee and bird behavior have offered insight into major problems in artificial intelligence.

For those not familiar, the examples discussed within the article are classic models in the science of complex systems. For example, here is the Netlogo implementation of bird flocking. It will run in your browser but requires Java 4.1 or higher. If you decide to take a look — please click setup – then go to make the model run. Once inside the Netlogo GUI, you can explore how various parameter configurations impact the model’s outcomes.

One of the major insights of the bird flocking model is how random starting conditions and local behavioral rules can lead to the emergence of observed behavioral patterns that appear (at least on first glance) to be orchestrated by some sort of top down command structure.

This is, of course, not the case. The model is bottom up and not top down. Both the simplicity and the bottom up flavor of the model are apparent when you explore the model’s code. For those interested, I will take a second and plug the slides from my ICPSR class. In the class, I dedicated about an hour of class time to bird flocking model. Click here for the slides. In the slides, I walk through some of the important features of the code (discussion starts on slide 16).

Introduction to Computing for Complex Systems — ICPSR 2010 — My Full Course Slides Available Online!

dmartink — Sat, 14 Aug 2010 03:49:46 +0000

I am going to bump this post to front of the blog one last time. We have now completed the full four week class here at the ICPSR Summer Program in Quantitative Methods. In this course, I (together with my colleagues) highlight the methods of complex systems as well as several environments designed to explore the field. These include Netlogo (agent based models and network models), Vensim (system dynamics / ecological modeling) and Pajek (empirical network analysis). In the final week, we cover a variety of advanced topics:

(a) Community Detection in Networks
(b) Computational Linguistics / Natural Language Processing
(c) Diffusion Models and Mathematical Modeling with Data
(d) Exponential Random Graph (p*) Models
(e) Information Retrieval / Webscraping

Although, we do not work with more advanced languages within the course, those who need to conduct complex analysis are directed to alternatives such as R, Python, Java, etc.

Anyway, the slides are designed to be fully self-contained and thus allow for individually paced study of the relevant material. If you work through the slides carefully you should be able to learn the software as well as many of the core principles associated with the science of complex systems. The material should be available online indefinitely. If you have questions, feel free to email me.

P ≠ NP ? [ Vinay Deolalikar from HP Labs Publishes His Proof to the Web, $1Million Clay Institute Prize May Very Well Await ]

dmartink — Mon, 09 Aug 2010 00:00:06 +0000

UPDATED VERSION HAS BEEN PUBLISHED TO THE WEB (August 9, 2010) [Click Here!]

After sending his paper to several leading researchers in the field and acquiring support, Vinay Deolalikar from HP Labs has recently published P ≠ NP to the web. While it has yet to be externally verified by folks such as the Clay Mathematics Institute, it looks very promising. Indeed, this very well represent a Millennium Prize for Mr. Deolalikar. For those interested in additional information, check out Greg Baker’s blog (which broke the story). Very exciting!

For initial thoughts on the matter by Dick Lipton, see the latest post on the blog Gödel’s Lost Letter and P=NP.

To read more about the history and importance of P vs. NP, please consult these sources:

P versus NP Problem (Wikipedia)
Stephen Cook, The P versus NP Problem
Madhu Sudan, The P versus NP Problem
Lance Fortnow, The Status of the P versus NP Problem
Stephen Cook, The Importance of the P versus NP Question

“Agents of Change” — Agent Based Models and Methods [ Via The Economist ]

dmartink — Tue, 27 Jul 2010 06:41:30 +0000

This week’s “economic focus” in the Economist highlights Agent Based Modeling as an alternative to traditional economic models and methods. As I am currently teaching Agent Based approaches to modeling as part of the ICPSR Introduction to Computing for Complex Systems, I am quite pleased to see this coverage. Indeed, the timing could not be better and I plan to highlight this article in the course!

Here are some highlights from the article: “… Agent-based modelling does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends. Crucially, agents’ behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This feature of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms …” For those who are interested, I have made similar points in the post “Complex Models for Dynamic Time Evolving Landscapes –or– Herb Gintis Offers a Strong Rebuke of “Meltdown.“

Julian Assange: Why the World Needs WikiLeaks [ TED 2010 ]

dmartink — Tue, 20 Jul 2010 16:59:48 +0000

Love it or hate it … WikiLeaks has been in the news quite a bit lately. In this TED talk, Founder Julian Assange sits down with Chris Anderson to discuss WikiLeaks. From the talk description … “The controversial website WikiLeaks collects and posts highly classified documents and video. Founder Julian Assange, who’s reportedly being sought for questioning by US authorities, talks to TED’s Chris Anderson about how the site operates, what it has accomplished — and what drives him. The interview includes graphic footage of a recent US airstrike in Baghdad.”

The Google Prediction API [From Google Labs]

dmartink — Mon, 19 Jul 2010 04:01:25 +0000

Harambeenet 2010 @ Duke Computer Science Department

dmartink — Thu, 08 Jul 2010 13:30:54 +0000

Today — Mike, Jon and I are at the 2010 Harambeenet Conference here in the Duke Computer Science Department. The conference is centered upon network science and computer science education. It features lots of interdisciplinary scholarship and applications of computer science techniques in novel domains. We are looking forward to an interesting couple of days of discussion!

John Underkoffler Points to the Future of UI [ Ted 2010 ]

dmartink — Thu, 24 Jun 2010 02:36:29 +0000

“When Tom Cruise put on his data glove and started whooshing through video clips of future crimes, how many of us felt the stirrings of geek lust? This iconic scene in Minority Report marked a change in popular thinking about interfaces — showing how sexy it could be to use natural gestures, without keyboard, mouse or command line. John Underkoffler led the team that came up with this interface, called the g-speak Spatial Operating Environment. His company, Oblong Industries, was founded to move g-speak into the real world. Oblong is building apps for aerospace, bioinformatics, video editing and more. But the big vision is ubiquity: g-speak on every laptop, every desktop, every microwave oven, TV, dashboard. ‘It has to be like this,” he says. “We all of us every day feel that. We build starting there. We want to change it all.’ Before founding Oblong, Underkoffler spent 15 years at MIT’s Media Laboratory, working in holography, animation and visualization techniques, and building the I/O Bulb and Luminous Room Systems.”

Complex Models for Dynamic Time Evolving Landscapes –or– Herb Gintis Offers a Strong Rebuke of “Meltdown” by Thomas Woods

dmartink — Wed, 23 Jun 2010 00:07:26 +0000

As highlighted on Marginal Revolution, economist Herb Gintis has authored an Amazon.com review of the book ”Meltdown: A Free-Market Look at Why the Stock Market Collapsed, the Economy Tanked, and Government Bailouts Will Make Things Worse” by Thomas E. Woods Jr. Suffice to say, the review is not flattering. Those interested in the direct attack on the book can read the full review here. Our particular interest in his review lies in the last third of the text where Professor Gintis highlights the genuine weaknesses of current macroeconomic theory. Below is the relevant text:

“I am often asked why macroeconomic theory is in such an awful state. The answer is simple. The basic model of the market economy was laid out by Leon Walras in the 1870′s, and its equilibrium properties were well established by the mid-1960′s. However, no one has succeeded in establishing its dynamical properties out of equilibrium. But macroeconomic theory is about dynamics, not equilibrium, and hence macroeconomics has managed to subsist only by ignoring general equilibrium in favor of toy models with a few actors and a couple of goods. Macroeconomics exists today because we desperately need macro models for policy purposes, so we invent toy models with zero predictive value that allow us to tell reasonable policy stories, the cogency of which are based on historical experience, not theory.

I think it likely that macroeconomics will not become scientifically presentable until we realize that a market economy is a complex dynamic nonlinear system, and we start to use the techniques of complexity analysis to model it. I present my arguments in Herbert Gintis, “The Dynamics of General Equilibrium“, Economic Journal 117 (2007):1289-1309.

While we do not necessarily agree with every point made in his review, the general thrust of the above argument is directly in line with the thinking of many here at the Center for the Study of Complex Systems. Indeed, the rebuke offered above could be extended and applied to other work in Economics and Political Science. A significant part of the problem is that the analytical apparatus in question is simply not up to the complexity of the relevant problems. Most of the current approaches derive from an era when a CPU had a transistor count of less than 10k and memory was exceedingly expensive. It is not as though leading scholars of the day were completely unaware that most systems are far more intricate than a “few actors and a few goods.” However, tractability concerns created a strong incentive to develop models which could be solved analytically.

Moderately high-end machines now have transistor counts of greater than 2,000,000,000 and memory is incredibly cheap (see generally Moore’s Law). No need to impose fixed point equilibrium assumptions when there is no qualitative justification for eliminating the possibility that limit cycle attractors, strange attractors or some class of dynamics are, in fact, the genuine dynamics of the system. We have previously highlighted the press release “What Computer Science Can Teach Economics“ (and other social sciences). This is really important work. However, it is really only the beginning.

More realistic representations of these complex systems are possible, however, it requires scholars to consider jettisoning analytical approaches/solutions. When modeling complex adaptive systems far more granularity is possible but this requires a direct consideration of questions of computation and computational complexity. The use of a computational heuristic is really not that problematic and it can help sidestep truly hard problems (i.e. NP Complete and the like). The difficult question is how and under what conditions one should select among the available set of such heuristics.

It is important to note, the dominant paradigm was itself a heuristic representation of agent behavior (and a useful one). While there are still some true believers, a declining number of serious scholars still assert that individuals are actually perfect rational maximizers. At best, this assumption is a useful guidepost for agent behavior and is one which can be subjected to revision by continued work in behavioral economics and neuroeconomics.

For those looking for a genuine intellectual arbitrage opportunity … the path is clear … devote your time to filling the space as this is a space with significant potential returns. The way forward is to remix traditional approaches with leading findings in neuroscience, psychology, institutional analysis and most importantly computer science … winner gets a call from Sweden in about t+25.

The IBM Watson Supercomputer – Artificial Intelligence Confronts Jeopardy – With Many Other Potential Applications

dmartink — Sun, 20 Jun 2010 04:18:20 +0000

Claude Shannon – Father of Information Theory

dmartink — Thu, 17 Jun 2010 04:01:49 +0000

This summer in the Complex Systems Advanced Academic Workshop we are devoting attention to information theory. In collecting some materials about Claude Shannon, I came across the above video and thought I would share it with others. Here is the description … “Considered the founding father of the electronic communication age, Claude Shannon’s work ushered in the Digital Revolution. This fascinating program explores his life and the major influence his work had on today’s digital world through interviews with his friends and colleagues.”