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Modeling Opinion Flow in Humans Using Boids Algorithm & Social Network Analysis

September 28, 2006 Article Start Page 1 of 8 Next
 

Introduction

Given the opinions and desires of a non-player character (actors), it is possible to devise a cost-benefit calculation to decide what they are likely to do. This is a common problem in Game AI and much good work has already been done on this. But this supposes a fixed set of opinions (beliefs) in the actors. We would like to allow the actors to evolve and change their opinions over time, just as real people do. We also want to replicate the fact that while the opinions people hold are often understandable, they are not always rational. In this paper we introduce a methodology to do just this.

Modeling opinion flow is a big topic. People’s opinions are understandably multi-faceted and complex. Here we are saying dash to this complexity and reducing the decisions on one particular issue (the topic at hand) to one simple number. At the end of the day in our game universe, one supports King John, supports King Richard, or doesn’t particularly support anyone. If the bulk of the population supports King John, then his troops will receive more resources – and that is an effect that can be felt by King Richard1.

To perform our calculation, we are borrowing concepts from the Boids algorithm and from Social Network Analysis. This technique makes possible new types of conflict, such as a Public Relations battle, and can make concrete the ‘battle for hearts and minds.’

People's opinions are influenced by events, but also by what they perceive to be the opinions of the people around them — people tend to believe what the people around them believe. The central analogy of this paper is that just as birds, fish, and other animals move their bodies in groups, humans move their opinions in groups2. Animals flock with their bodies. People flock in their opinions.


A bird that strays from the flock
will change its course to move
back toward the flock, even as the
flock may begin to veer toward it.
Most people feel uncomfortable if
their thinking is too far unaligned
from that of the group, and will try
(either by trying to change the
group or their own thinking) to
minimize that distance.

This technique can be applied to large populations or small populations. A large population example could be an entire population of a country and their support of a particular armed militia group. (If the player can reduce public support for the militia, its resources will decrease.) A small population example could be the actors around a key decision maker. (If the player can locate and change the opinions of the people around the decision maker, it will be possible to influence the decision maker.) Both examples will be explored here.

Brief Explanation of Boids algorithm

Boids Algorithm, with only a few simple parameters, accurately replicates the movement of flocks of birds and schools of fishes. The movement appears so realistic that it won an Academy Award in 1987 in the movie “Batman Returns.”

Highly complex behavior can be explained by the simple reactions of many independent agents all working with incomplete information. The movement of an opinion in a society may also appear to be incredibly complex, but it also the result of many independent agents making straightforward calculations with incomplete information.

In Table 1 is a mapping of the Boids Effect, and the effect on thought as modeled here.

Force

Boids Effect

Effect on Thought

Alignment

Birds steer in the general direction of the group.

People tend to give credence to an idea shared by many people. They build their picture of what the ‘group’ thinks based on information from their news sources.

Cohesion

Birds move toward their near by flock mates.

People tend toward the ideas of the people they respect.

Separation

Birds maintain a safe distance from their flock mates.

People maintain a distance from ideas held by people for whom they have contempt.

Table 1 . Boids Effect vs. Effect on Human Thought


In a sense we are applying Newton’s second law of motion (Force = mass * acceleration)
to ‘ideas’ which have no mass. Ideas do, however, have a velocity. Sometimes ideas move fast – such as 'my
Internet stock isn’t worth as much as I thought it was. I better sell.’ Sometimes ideas move slowly – such as ‘women should have the right to vote.’ Although universal suffrage may now seem like common sense to most of us, it was an idea that took centuries to become accepted.

Boids is a physical model and the Boids effects create forces, which influence the movement of the animal. For example, a bird seeing that it is falling behind will begin to accelerate until it catches up with the flock.

Our forces will provide a propensity for the opinion of an individual to move in a particular direction. If there are people close to me that hold a particular opinion, then I will be likely to hold that same opinion. If I hold a different opinion, I will have to mentally justify it to myself in some way (such as thinking I have information that they don’t have.) The brain is a muscle, and mental justifications don’t come free. So the easy thing, and the typical thing, is to just give in and accept the ‘conventional wisdom’ and to fly with the rest of the flock.

There is a separate, and quite important, consideration in the Boids algorithm. The birds only have information regarding the other birds in their field of vision. We will model this by providing all of the members of our virtual society a connectivity score to all of the other members. This type of approach is common in social network analysis, and is explained further in the next section.


Article Start Page 1 of 8 Next

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Comments


Sam Cartwright
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Just a minor mistake: Batman Returns was released in 92. The original paper on Boids was written by Craig Reynolds in 87, and the academy award was awarded in 98 (I believe mainly based on its use in Batman Returns, as well as in other films)


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