Thursday, June 30, 2005

Modelling environmentalism thinking

It's a human political belief model -- based on Cyc! I'm not sure logic represents how people think all that well, but seeing the formalization of ideology is fascinating. And besides, the methodology of cognitive modelling is awesome. The link:
Modeling How People Think About Sustainability

David C. James, M. P. Aff

LBJ School of Public Affairs
The University of Texas at Austin
May 2005

First Reader: Lodis Rhodes
Second Reader: Chandler Stolp

How effectively can a computer model represent the belief systems of different people? How would one go about representing a belief system using formal logic? How would that ideology react to different scenarios related to sustainable development? The author constructs the Cyc Agent-Scenario (CAS) model as a way to investigate these questions. The CAS model is built on top of ResearchCyc, a knowledge base (KB) and logical inference engine. The model consists of two agents (Libertarian and Green) and two scenarios. The model simulates how agents would think about or judge the scenarios. Theoretical and practical concerns of modeling, logic, rationality, and emotion are discussed. The CAS model is compared against other approaches, such as econometrics and polling, that are useful for public policy practitioners. Lastly, potential applications of the model for democratic deliberation, negotiation, planning, and participation are explored.

Sunday, June 26, 2005

monkey economics (and brothels)

This is a fun one: researchers trained capuchin monkeys to understand tokens as currency by letting them exchange them for food. Then they did all sorts of behavioral economics-y tests like finding consistency of preferences revealed in price shocks. The monkeys even displayed loss aversion at rates almost identical to humans! And along the way they got "prostitution":

Something else happened during that chaotic scene, something that convinced Chen of the monkeys’ true grasp of money. Perhaps the most distinguishing characteristic of money, after all, is its fungibility, the fact that it can be used to buy not just food but anything. During the chaos in the monkey cage, Chen saw something out of the corner of his eye that he would later try to play down but in his heart of hearts he knew to be true. What he witnessed was probably the first observed exchange of money for sex in the history of monkeykind. (Further proof that the monkeys truly understood money: the monkey who was paid for sex immediately traded the token in for a grape.)

Mark Liberman interestingly points out the monkeys seem to be an exception to Adam Smith's assertion that animals don't do trades; but since capuchins still have simple cognition and no language abilities, the human behavior of trade can't be based on uniquely human cognitive capabilities like Smith argues.

Saturday, June 25, 2005

more argumentation & AI/formal modelling links

ArgMAS (argumentation in multi-agent systems) 2005
Computational Models of Natural Argument (CMNA) 2005

Conferences in Edinburgh and the Netherlands, seems to make sense.


This is fairly funny, by good ol' Jaron Lanier on that good ol' topic, AI and philosophy: You can't argue with a zombie

Thanks to neurodudes.

looking for related blogs/links

What are good other resources on the internet for social science, cognitive science, and artificial intelligence (or computation more generally)? I'm looking for blog-like things in particular -- stay updated on new research and the like.

here's the list so far, trying to be interdisciplinary as possible. A cognitive neuroscience or neuroeconomics blog would be a nice addition.

other possibilities... need to search for more...
  • neurodudes

Perhaps mailing lists and/or newsgroups are better for some of these topics.

idea: Morals are heuristics for socially optimal behavior

A common cognitive science view (H. Simon):
Heuristics/biases are useful rules-of-thumb to approximate optimizing behavior given computational constraints.

Consider the chess-playing heuristic "try not to lose your queen". Since you can't analyze all possible chess moves, it's nice to have such a rule-of-thumb to narrow down possible actions to consider. You can reject out of hand an action that leads to losing your queen. This heuristic helps to approximate optimal chess-playing behavior given your computational constraints.

Similarly, moral rules, tendencies, associations, and ontologies are heuristics to approximate socially optimal behavior. "Lying is bad" is a useful rule-of-thumb that usually gets good results for society. Codifying it as a norm -- meaning, there's 3rd party punishment and/or self-punishment (guilt) when it's violated, thus the rule should get obeyed -- is the implementation of a social-level heuristic that generally gives useful behavior.

Just like this Simonian definition of a heuristic, "Lying is bad" is necessary due to computational and informational limitations. You can't foresee very well that lying could cause trouble down the road. In fact, you may be pretty sure it could give good results in the short-term. However, since it's usually actually bad, a norm against it may be overall socially beneficial. Then for learning or group selection reasons (people imitate successful strategies, groups with the norm outcompete other groups), a socially beneficial norm may take root and spread.

That does not mean "lying is bad" is some sort of universal truth, or even that it can be evaluated as a truth-functional statement (that is, must resolve to true/false in a certain world). That would imply there is are actually definite sets of good and bad things. Instead, it may be useful to think of a fictionalist explanation: this moral ontology of goodness/badness is a pragmatically useful fiction (useful because it helps bring about a just and orderly world.) Specifically, it does this by functioning as a computational shortcut.

Of course, whether morals are real or not should be irrelevant for a behavioral analysis of morality, but that issue always seems to get dragged in anyway, perhaps because we seem to care about it a lot.

Friday, June 24, 2005

1st International Conference on Computational Models of Argument (COMMA06)

does this look awesome or what? Imagine if you could computationally model the argumentation and communication in economic and political behavior. To say nothing of the AI applications too!

1st International Conference on Computational Models of Argument (COMMA06)

Organised by the ASPIC project (
The University of Liverpool, Liverpool, UK
11th-12th September 2006 (provisional)

General Chair: Professor Michael J. Wooldridge
Programme Chair: Paul E. Dunne

Over the past decade argumentation has become increasingly important in Artificial Intelligence. It has provided a fruitful way of approaching non-monotonic and defeasible reasoning, deliberation about action, and agent communication scenarios such as negotiation. In application domains such as law, medicine and e-democracy it has come to be seen as an essential part of the reasoning.

Successful workshops have been associated with major Artificial Intelligence Conferences, notably the workshop series on Computational Models of Natural Argument held in conjunction with IJCAI and ECAI, and the series of ArgMAS workshops held in conjunction with AAMAS. The time is now right for a conference dedicated to all aspects of computational argument.

Topics include, but are not limited to:
* argumentation frameworks
* argument schemes
* argument in agent systems
* argument based negotiation
* computational properties of argumentation
* decision making based on argumentation
* dialogue based on argument
* e-democracy applications
* legal applications of argument
* medical applications of argument
* reasoning and argument about action.

A call for paper will be issued shortly. To be placed on an electronic mailing list for this conference e-mail:

This entry was posted on Monday, June 6th, 2005 at 6:55 pm and is filed under