Tag Archives: Behavior

Humans Acting Like Chimps

The video below is titled, “Chimpanzee Human-Like Behavior Montage.”  Of course, we only think of their behavior as “human-like” because we’re human.  I’m sure they are watching us on Youtube thinking, “Man, those humans sure act like Chimpanzees!’” 

Emergent Behavior Emerging Everywhere!

Chris Rollins has a great post on Emergent Behavior on Scientific Blogging.

He makes a great series of points hear about the relationship between emergent behavior and entropy, and how they can possibly coexist:

Emergent behavior, or the spontaneous creation of order, is present all around us. Insects are a good example because they are familiar to us and manage to undertake massive building projects which we can appreciate. Other parts of the animal kingdom also display autonomous order – fish organize themselves into schools that move in concert; birds and pack animals flock or herd in a similar manner. And nonliving examples also abound – natural magnets align themselves into a common North-South orientation and crystals can form from liquids, showing a spontaneous increase in order despite the lack of a more “intelligent” force.

There’s a major thermodynamic problem with all of this, of course – entropy, a measure of disorder, is supposed to continually increase. The universe tends toward chaos.

How, then, can spontaneous order arise, especially in a purely physical system?

Entropy still must increase, even when crystals form or birds flock, but the important distinction is in where the entropy increases or decreases. It turns out that nature will allow entropy to decrease in certain areas provided that it increases elsewhere to compensate. For instance, when a sugar solution begins forming crystals those crystals have a net lower energy level than the free-floating molecules of sugar in the solution. When the sugar enters the structure, it loses energy that is transferred to the water in the form of heat: the most disorganized form of energy.

Therefore, the crystalline portion of the solution has now decreased in entropy while the total system – including the water and the crystal – has had a net increase in entropy.

A Review: The Hitchhiker’s Guide to Altruism: Gene-culture Coevolution and the Internalization of Norms

In an article for the Journal of Theoretical Biology, Herbert Gintis provides a model that shows that:

“if an internal norm is fitness enhancing, then for plausible patterns of socialization, the allele for the internalization of norms is evolutionarily stable.”

Further he posits that this view can be used to model Herbert Simon’s 1990 explanation of altruism.  Simon’s idea is that altruistic norms “Hitchhike” on the backs of the general tendencies of internal norms to be “personally fitness enhancing”.

OK, cool, so what is an “internal norm”?  It’s a pattern of behavior that is regulated by internal controls or “sanctions”.  We’d think of these like shame or guilt, the little guy sitting on your shoulder with the white outfit arguing against the little guy on the other shoulder in the red outfit.

Gintis makes the point that our very capacity to internalize norms like guilt and shame means that humans have what he calls “socially programmable” objective functions.  An objective function is an economics idea.  The point is that humans have these functions (that we can objectively model) that they try to optimize (or maximize) subject to particular constraints.

So, human behavior is dependent not only on the beliefs they hold about their objective functions (if I choose to take action A, then the outcome is result R), but also on their “values”.  The values are the goals, or the point of why they would take a particular action in the first place.

He goes on:

“Suppose there is one genetic locus that controls the capacity to internalize norms.  I develop models of gene-cultural coevolution to show that if an internal norm is fitness enhancing, then the allele for internalization of norms is evolutionarily stable.  Moreover, if the fitness payoff to the internalized norm is sufficiently large, or if there is a sufficiently high rate of phenotypic level assortative mating, the allele for internalization is globally stable.”

It’s important to discuss what he means by “evolutionarily stable”.  The idea was developed by John Maynard Smith.  In his book, “Evolution and the Theory of Games.”, he describes it as such:

“A ‘strategy’ is a behavioral phenotype; ie, it is a specification of what an individual will do in any situation in which it may find itself.  An ESS (Evolutionarily Stable Strategy) is a strategy such that, if all the members of a population adopt it, then no mutant strategy could invade the population under the influence of natural selection.”

Gintis has been spearheading a move for the past 20 or 30 years to make evolutionary game theory (and the use of the ESS) the standard in Social Science research modeling, as opposed to classical game theory.  But, that’s another topic.

He is attempting to make a case, in this article, that people value behavior that can be described as altruistic (bravery, fairness, empathy, etc) for its own sake.  This is in contrast to the notion that people are altruistic because in the long term it is in their best interest.  This means that:

“A person who has internalized the value of ‘speaking truthfully’ will do so even in cases where the net payoff to speaking truthfully would otherwise be negative.”

Further, he argues that a norm will be more prevalent in a population if it is internalized by a the individuals in that population than if it is only followed when those individuals think it is in their best interest.

He uses his model to elucidate the way in which altruistic internal norms can drive out norms that are harmful both socially and to the individual.  He finds that altruistic cooperation and punishment (in his model) don’t depend on “repeated interaction, reputation effects, or multi-level selection”.  Even though an individual who caries the gene for altruistic behavior and the altruistic norm can suffer a personal cost (as per the definition of altruism), it doesn’t negatively affect the group.  That is, there is no in-group net negative effect.  It is in fact because of this pro-social effect that the strategy of the altruistic norm can be an ESS.

Basically, Gintis provides a model that shows that it is possible to fix an altruistic gene in a population for particular types of behavior that are good for the group as a whole, even through they hurt the individual.


  • Gintis, Herbert.  2003.  “The Hitchhiker’s Guide to Altruism: Gene-culture Coevolution, and the Internalization of Norms.”  Journal of Theoretical Biology. 220, 407-418.
  • Smith, John Maynard.  1982.  Evolution and the Theory of Games. Cambridge University Press.

Poliheuristic Theory: An Introduction

This post is part of a series of posts I’m working on covering some basic models in Decision Theory.  For my previous post on Cognitive models, click Here.

Decision theory in foreign policy analysis has been characterized by a split between two different, and at times rival, models of human behavior (James and Zhang, 2005).  The first is the classical model of Rational Choice theory, a theory that takes as its starting point the end of the decision making process and attempts to figure out why the choice was finally made.  The second is the Cognitive approach, a theory that focuses on the “how” questions of decision making and attempts to reconstruct why the end outcome occurred.  In an attempt to integrate these two different, but useful, approaches, Alex Mintz created Poliheuristic Theory (PH) (Mintz 2005).

The model consists of two stages (Mintz, 2005).  The first is the “heuristic” stage.  In this stage the actor uses heuristics, or simple tools of thought, to limit the number of choices available to him.  This is similar in character to Robert Axelrod’s Schema Theory (1973) and other cognitive approaches (Simon, 1955; Tversky and Kahneman, 1981, 1986, and 1991), including Prospect Theory (Levi, 1997; Tversky and Kahneman, 1992).   The second stage is the “evaluation” or “calculation” stage.  Here the actor makes calculations based on the given information garnered from the first stage.

The components of the first stage culminate in a “decision matrix” that has a number of parts: alternatives, dimensions, implications, ratings, and weights.  The alternatives are the choices available to the actor.  The dimensions are the relevant criteria used to evaluate the different alternatives.  The implications are what happens at each pairing of the alternatives with the dimensions.  Ratings can be given to each of the implications to aid in analysis for the researcher.  The weights are the relative “importance level” of the different dimensions.

The second stage takes the information given in the first stage and analyzes the different outcomes associated with the given values.  This stage resembles classical Rational Choice Theory (Arrow, 1959), and uses the Expected Utility Principle to justify which options are the most viable.

The theory has been successfully applied to Presidential decisions, including Bill Clinton’s bombing of Kosovo (Redd, 2005) and Jimmy Carter’s decisions during the Iranian hostage crisis (Brulé, 2005), and even to autocratic regimes (Kinne, 2005).

For readers with knowledge of Game Theory, there may seem to be a number of similarities between it and the PH model.  But, these similarities are surface deep.  Among them is the use of a decision matrix in the PH model.  Game Theory employs a similar matrix, called the “strategic form” of the game (Gates and Humes, 1997; Gintis, 2000; Rapoport, 1966).  Another is the rating of implications in PH theory with numerical values that can then be assessed mathematically.  Game Theory uses the same devise to make calculation easier-and possible.  But, the differences are more important.  PH theory is an attempt to understand both why and how a particular actor came to a decision.  Game Theory, by contrast, is interested in the dynamics of interactions between and among actors in a given situation.  Game Theory does have methods of evaluating why a choice was made based on expected value, but not how it was made.

However, PH theory and Game Theory are compatible and may, if used together, provide a powerful method of analyzing the decisions of actors in relation with other actors.  Game Theory can illuminate the potential outcomes resulting from the interaction of the players, and PH theory can explain how and why any particular player would make the choices they would make.  This is an advantage over the traditional use of Rational Choice Theory with Game Theory which presumes players are bound by a careful analysis of their expected utility, when in fact, many people do not respond this way in the “real” world (Camerer, 2003).  It is also an advantage to using only the cognitive approach, which tends to shun rational choice thinking as too unrealistic, when in fact, once an actor (in the PH version) has narrowed down their list of actions, they are more likely to make a choice based on the expected utility principle (Redd, 2005).

Poliheuristic Theory is a welcome addition to the literature, and toolbox, of Political Scientists, Economists, and Social Scientists generally.  It bridges the gap between the two primary perspectives in the field, Rational Choice and the Cognitive Models, and provides an easy to use framework for analysis.  Further research may prove that it will work well with Game Theory as a positive approach to understanding why decisions were made in complex interactions among agents.


  • Arrow, Kenneth J.. 1959. “Rational Choice Functions and Orderings.”  Economica.  New Series, Vol. 26, No. 102, pp 121-127.
  • Axelrod, Robert.  1973.  “Schema Theory:  An Information Processing Model of Perception and Cognition.” The American Political Science Review, Vol. 67, No. 4, 1248-1266.
  • Brulé, David J.. 2005.  “Explaining and Forecasting Leaders’ Decisions: A Poliheuristic Analysis of the Iran Hostage Rescue Decision.”  International Studies Perspectives. 6, 99-113.
  • Camerer, Colin F.. 2003.  Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press.
  • Gates, Scott and Humes, Brian D.. 1997.  Games, Information, and Politics: Applying Game Theoretic Models to Political Science. University of Michigan Press.
  • Gintis, Herbert.  2000.  Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction. Princeton University Press.
  • Kinne, Brandon J.  2005. “Decision Making in Autocratic Regimes: A Poliheuristic Perspective.” International Studies Perspectives.  6, 114-128.
  • Levi, Jack S.. 1997. “Prospect Theory, Rational Choice, and International Relations.” International Studies Quarterly.  Vol.41, No.1, 87-112.
  • Mintz, Alex.  2005.  “Applied Decision Analysis: Utilizing Poliheuristic Theory to Explain and Predict Foreign Policy and National Security Decisions.” International Studies Perspectives. 6, 94-98.
  • Rapoport, Anatol.  1966.  Two-Person Game Theory.  Dover Publications, Inc.
  • Redd, Steven B.. 2005. “The Influence of Advisers and Decision Strategies on Foreign Policy Choices: President Clinton’s Decision to Use Force in Kosovo.”  International Studies Perspectives.  6, 129-150.
  • Simon, Herbert A. 1955.  “A Behavioral Model of Rational Choice.” The Quarterly Journal of Economics, Vol. 69, No. 1, 99-118.
  • Tversky, Amos and Kahneman, Daniel.  1981.  “The Framing of Decisions and the Psychology of Choice.” Science, New Series, Vol. 211, No. 4481, 453-458.
  • Tversky, Amos and Kahneman, Daniel.  1986.  “Rational Choice and the Framing of Decisions.” The Journal of Business, Vol. 59, No. 4, Part 2: The Behavioral Foundations of Economic Theory, S251-S278.
  • Tversky, Amos and Kahneman, Daniel.  1991.  “Loss Aversion in Riskless Choice: A Reference-Dependent Model.” The Quarterly Journal of Economics, Vol. 106, No. 4, 1039-1061.
  • Tversky, Amos and Kahneman, Daniel.  1992. “Advances in Prospect Theory: Cumulative Representation of Uncertainty.” Journal of Risk and Uncertainty. 5:297-323.
  • James, Patrick and Zhang, Enyu.  2005.  “Chinese Choices:  A Poliheuristic Analysis of Foreign Policy Crises.”  Foreign Policy Analysis.  1, 31-5