Collective Decision Making: From Neurons to Societies - SOS

Workshop

Self Organized Science

For the afternoon sessions we will allow everyone to self organize around key topics related to the theme of collective decision making. Anyone who wants to is welcome to identify an issue or opportunity related to the theme that they care about exploring with others. Along with announcing and posting the idea to the group, the person must take responsibility for convening the group and insuring that the group's discussions get reported on the wiki. Please note that everyone should feel free to vote with their feet: if you are not learning or contributing to the group that you are in, feel free to go to another group at any time. (The idea of using self organization for setting agendas was inspired by work in complex systems and has been encapsulated by the somewhat confusing term "Open Space Technology.")

Report from Turing Test for Superorganisms

DeFroment, J. Miller, Morewedge, and Oppenheimer

We began by debating whether or not one can design a Turing test for a group or superorganism. After some debate, we realized that it might be better to try to design tests of intelligence along a gradient rather than use humans as a benchmark. This would allow one to test a few different interesting questions:

1. Is this group as "smart" as a human?

2. Is group A "smarter" than group B?

3. Is this group "smarter" than an individual member of the group? Does it learn more quickly, demonstrate less noise, etc.?

4. Can the group compare alternatives along more than one dimension (e.g., amount of reward/probability of reward)?

5. How robust is the superorganism? That is, can it survive in novel environments.

6. Can the group deceive another group?

7. Does the group exhibit strategic behavior? How many levels of reasoning can the colony go to?

8. If the majority of the group holds a false belief (e.g., is deceived by an experimenter), can a minority of group members correct this false belief?

9. Can the group engage in generalization of learning?

We also explored the idea of testing whether a superorganism can learn in the same way that an individual organism does.

1. Are there analogs of learning experiments on, say, conditioning pigeons, that can be applied to ant colonies? For example, can you condition an ant colony to novel stimuli (such as a novel chemical signals) to behave in different ways?

2. Are there analogies between types of ant tasks and different kinds of physiological systems (patrollers as sense organs, foragers as hands, ??? as short term and long term memory)? Is there transactive memory in a colony (is it organized among individual members)?

3. Is there a collective reward structure? In other words, if one member is rewarded for another member's behavior, does the non-rewarded member continue to perform that behavior?

4. Does the colony use some kind of physical memory--do interior properties of a colony reflect the outside food sources (e.g., location of midden piles)?

5. Is there cultural learning? If you remove the members of the group that learned a specific behavior (e.g., association between a reward and a novel pheromone), does the group still retain the association when you remove the specific members who perceived the reward and stimulus first-hand?

Colony "hard" problems. If colonies work by having individuals with simple sets of interacting programs, it would be insightful to find circumstances under which those programs break down.

Report from Reliability of Collective Decision Making

Participants: Iain Couzin, Nigel Franks, Michael Mauboussin, Peter Miller, Kevin Zollman

This group was interested in discussing how reliable collective decision making groups are, and if possible what features appeared to support that reliability. In the discussion several interesting points were noted.

Collective decision making groups (both non-human animal and human) are often very effective in "regular environments". However, this reliability doesn't always extend to rare or manipulated environments. Ants can be made to run around in circles, fish can be made to swim directly toward a predator, markets can over adjust, etc.
In most situations it seems that there is a trade off between speed and reliability. Obviously this is an optimization problem where the right solution depends on features of the environment. There was some interest in comparing how well these systems do when compared to more computationally complex optimal algorithms.
- Are they well tuned to a small set of environments or all they robust to a relatively wide range of environmental circumstances?
Variance can often have an important effect on that reliability. For example, individual ants look for different nesting sites and different bees may give different assessments of the same site. This variance might be the result of individual's having high variance in their individual assessments or might be the result of largely heterogeneous populations with individuals who have individually low variance in their assessments.
- There was significant interest in determining if the variance was the result of variance within individuals or heterogeneous groups.
  - In either case, understanding the mechanisms which produce and sustain this variation is an interesting topic of study.
- Given features of the environment, it would be interesting to know how "well tuned" this variation is. In some situations the variance appears to change on the basis of environmental features. So groups (or individuals) tune their search algorithm to the environment.

Report from Rule Sets in Human Collective Decision Making

Seeley,

Human stuff

Report from Group Size and Collective Decision Making

Deborah Gordon, Ana Sendova-Franks, Jeremiah Cohen, Nick Britton, Leo Sugrue

Analogies between effects of size on colony behavior and effects of size in neural systems?

Larger neural systems are more flexible. Why? More switches. What’s a switch? We don’t really know. More layers. What are the layers? Well, instead of a simple circuit, in which a stimulus activates one neuron which then leads to some action - there are more neurons in between. So is the flexibility due to more complicated structure in a larger brain, or just to more steps? We don’t really know.

In ant colonies there seems to be a threshold when the group is too small to function. The smaller the group, the more important individual variation may be. (Is individual variation important in neurons? We don’t know because it’s hard to identify the activity of particular nuerons within a system).

Discussed current experiments with ants by Ana Sendova-Franks, and past ones by Deborah Gordon, in which ants are removed. These show how decreasing the numbers performing a certain task (e.g. foragers) changes the behavior of the rest of the colony. In Gordon’s earlier experiments, removal of small numbers of ants performing one task shifted the numbers performing other tasks. Presumably removals lead to shifts in rate of interaction across task groups which leads individuals to switch task or modify activity level.

Leo told us about a neural network model by Carlos Brody which describes the interactions among large numbers of neurons, and shows how a change in the network leads to a change in behavior. The change is in weighting? Maybe this is an example of a detailed model for an effect of group size.