Cognitive Models of Decision Making: VOTE

A Realistic Model of Rationality


The economic model of rationality has a mathematical elegance and appeal. Unfortunately standard decision theory is not generally feasible as a computational psychological or social model of rationality. We propose an alternative model based on realistic assumptions of computational power psychological ability and social interaction. The traditional task for demonstrating rationality is decision making. We describe the VOTE program which simulates human decision making based on our model. We discuss the application of the decision making model to the related social multi agent phenomena of advice persuasion and negotiation.


There are many possible ways to view rationality.

Economic decision theory proposes a prescriptive mathematical model. Given knowledge of the options, probabilities, and payouts, decision theory can identify the choice with the highest expected value. According to economics, that is the rational choice.

However, outside of a casino the decision making agent rarely knows the probabilities or payouts for a given choice. The agent may not even know what all the options are. In certain circumstances when all the information is available, the agent still may not have the computational ability to compute the optimal answer as in the game of chess. Evaluating all possible outcomes is not computationally feasible.

Simon discussed these problems and proposed bounded rationality which incorporates information processing constraints in an effort to reflect the limitations of human cognition. Simon recognized that an agent may lack processing capability or information and could not optimize a choice but rather must satisfice. Simon and Newell describe decision making as a search problem.

In this paper we present an alternative model of rationality in the tradition of bounded rationality intended to make realistic assumptions about the decision maker.

In making a decision the rational agent does not optimize or satisfice but rather justifies her choice. We make the following assumptions:

Since knowledge is considered a resource, an agent is not irrational if she fails to achieve a goal from lack of knowledge.

If an agent prefers car A to car B but cannot afford car A, then it may be rational for the agent to purchase the less expensive car B instead. Suppose she could have negotiated a better price for car A but did not. Her behavior is not irrational if she was unaware of the negotiation option. In this case lacking knowledge can be considered equivalent to lacking money. If she had more money she could have purchased car A. It is not irrational that she did not have more money.

It may seem odd to discuss emotions in the context of a model of rationality Emotional behavior is usually considered the antithesis of rational behavior. However using Roseman’s model it is possible to incorporate emotions as a means of reflecting an agent's state of goal pursuit.

Achieving a goal leads to a positive emotion such as happiness or pride whereas failing to achieve a goal results in a negative emotion such as hate or frustration. Furthermore the strength of the emotion reflects the importance of the related goal. An agent is passionate about what is most important.

In our view it would be irrational for an agent to display an inappropriate emotion when winning a gold medal or losing a child. Emotions contribute to the social dimension of rationality and decision making.

Adopted goals are processed in a uniform manner with intrinsic goals This process of goal adoption provides a principled model of social interaction. Agents may engage in cooperative or competitive behavior based on goals adopted through interpersonal relationships.

When a judge renders a verdict or a member of Congress votes on a bill or a corporate executive closes a plant the decision needs an explanation. There are many possible ways to arrive at a choice. The explanation tells us how the decision was made or at least how the agent wishes us to interpret her decision.


The VOTE decision making model is based on the explicit representation of goals, choices, relationships, and strategies, and the use of natural language to produce explanations.

The VOTE program simulates the roll call voting of members of the United States House of Representatives. Given a member of Congress and a specific bill, VOTE tries to determine how that member would vote and then produces a natural language explanation of the resulting decision in English or French.

Below is an example of the VOTE program simulating Congressman Morris Udall voting on a bill banning flag burning.

> (vote ‘udall ‘hr-2978)

* Member: Morris K Udall

* Bill: Flag Desecration

* Bill banning the desecration of the flag

--- omitting intermediate output ---

English rationale:

Morris K Udall votes against bill HR-2978 the flag desecration bill. After weighing the implications, he believes that provisions of this bill are not constitutional. He completely supports the United States Constitution and the Bill of Rights. Udall readily endorses the right of freedom of speech. Even so, Udall realizes that members of the Democratic party oppose the right of burning the American flag in protest.

French rationale:

Morris K Udall s’oppose au projet de loi HR-2978. la loi de la profanation du drapeau. Apres une consideration approfondie il croit que les dispositions de ce projet de loi ne sont pas constitutionelles. Il est un champion de la Constitution americaine et de la declaration des Droits. Udall desire vivement appuyer le droit de libre expression. Cedependant Udall comprend que les membres du parti Democratique s’opposent au fait de bruler le drapeau americain lors d’une manifestation.

The natural language explanation above is not canned text but is generated automatically by VOTE. Similarly the French text is not a translation of the English text but is generated from the underlying knowledge representations.

The VOTE program relies on a set of interrelated databases including issues (over 200 currently in the database), constituency groups (150), bills (42), members (67), and decision strategies (16). We note that multiple decision strategies are required since the explanation of the decision depends on the strategy employed. It is not enough to use one simple strategy of summing the weights of the conflicting issues and relationships.

The purpose of VOTE is not to predict individual voting decisions but rather to demonstrate the computational feasibility of a particular model of interpersonal relationships and decision making. Having said that we observe empirically that VOTEs accuracy rate on thousands of predictions exceeds 75%.

VOTE embodies the realistic decision making assumptions stated above.

We assert that VOTE’s decisions are rational even though they may not be optimal. Given the lack of complete knowledge in this domain, it would be irrational to assume the feasibility of achieving an optimal decision. What is rational is for a member to consider her preferences and those of her constituents, the consequences of the legislation, and the decisions explanation.

From the political science literature Kingdon notes that voting strategies often hinge on the role of explanation. Members of Congress report that for a given vote they either need to have a good explanation or avoid the vote that would require an explanation. Given that a member is elected by the voters of her district, her ideology and beliefs are likely to reflect those of her constituents. Thus, generally a member’s votes will not require explanations. Furthermore, once she has established a voting record she can avoid expla nation by being consistent in her future votes. That is, if a member votes on bill X the way in which she has always voted on similar bills in the past, then she should not have to explain that vote.

Kingdon quotes a representative who opposed a measure providing for the direct election of the president but nonetheless voted for it.

‘Very frankly if I had a chance to sit down with all my constituents for 15 minutes and talk to them I'd have voted against the whole thing. But I don't have that chance. They wanted to change. If I voted against it, it would appear to them that I was against change, and I wouldn't have a chance to explain myself.’

Kingdon notes that the importance or intensity of an issue can also affect the justification of a vote

The effect of this need to explain oneself is somewhat related to the weighing of intensities ... If the congressman feels intensely about the matter he will take the trouble to explain his position. If he does not feel so strongly, it is likely that he will avoid the situation in which he is obliged to explain by voting with his constituents. Because there are many occasions on which a segment of his constituency has strong preferences and the congressman's preferences are not so strong this tendency to avoid the uncomfortable confrontation probably contributes a good deal to effective representation of such interests.

In certain cases a vote may seem irrational. In Congress, a member may cast a vote that appears to violate the preferences of the member and her constituents.

For example, black members of Congress will occasionally vote against civil rights legislation. This action appears bewildering in the absence of an explanation. The black members can claim that they were registering a protest vote and wanted to encourage the passage of stronger legislation. Typically the protest votes do not result in stopping passage of the bill. Thus the members can have their cake and eat it too. The VOTE decision strategy Not Good Enough incorporates this explanation.

The need for explanations is a reflection of the fact that agents cannot make optimal decisions There are many possible decision strategies. A rational decision maker provides an explanation to illuminate her decision.

Multiagent Interaction

Decision making is usually viewed from the perspective of a single agent. The VOTE model suggests a principled way to enlarge decision making as a social process through the adoption of goals from interpersonal relationships.

There are other multi agent phenomena that are explicitly social that may be examined from our model of decision making

In each case, advice, persuasion, and negotiation, the parties need to understand each other's preferences and beliefs. The basic VOTE model of decision making provides the foundation for these other social interactions.

Realistic Irrationality

In presenting our model of rationality we have avoided the prescriptive view of good and bad decisions. If an agent has an internal set of goals and makes decisions consistent with those preferences we consider those choices to be rational. It would appear that almost any decision can be viewed as rational by this account.

This is a problem. How can you have rationality if there is no irrationality? Is there good without evil? Is there hot without cold? Can there be Democrats without Republicans? We know that people make bad decisions. The author has met such people.

We know that individual decision makers often seek out the advice of others when facing a difficult choice. It is possible to improve decisions. Our model can accommodate these data.

One cause of irrationality is due to the subjective frame of reference. In VOTE, a member of Congress may have a conflict in goals adopted from two constituency groups. The group on the losing side may view the decision as irrational from their frame of reference.

In addition to goals adopted through relationships there are societal norms: standard sets of preferences and beliefs to which an agent may subscribe when making a decision. For example economics provides a decision maker with an agenda derived from the laws of supply and demand which are usually summarized with the dictum maximize profits. Similarly most religions provide codes of ethical behavior such as the Ten Commandments suggesting that agents refrain from theft and murder Is it irrational for an executive to steal from or even kill her competitor? Society has decided that religion wins out over economics.

In many cases society has stipulated normative behavior. It is considered irrational if not illegal to violate these norms. There is an implicit rule that agents adhere to the norms of society. Subjective decision making has its limits. A decision which may be rational for a single agent becomes irrational in a social context.

An agent faced with a hard choice then has several reasons to get outside advice based on the fundamental assumption of limited knowledge. There are a number of questions which may arise.

A decision made in the absence of such information may be considered irrational. However, there is no axiomatic set of knowledge describing society's rational expectations.

We assume that an agent knows that by shooting a gun you can kill someone. We do not necessarily assume that by investing in derivatives you can lose a billion dollars.

Different groups and situations have different norms Common sense is the normative common denominator. Different norms exist for specific areas such as economics, law, medicine, sports, computer science, and artificial intelligence. What is rational for the lawyer may be irrational for the physician.

The irrationality of a decision depends on the societal norms. Earlier we argued that knowledge like money was a resource and that lacking knowledge did not necessarily make a decision irrational. We now qualify that statement by suggesting that society assumes a certain level of consensual knowledge that is a resource common to agents in similar circumstances.


We have discussed a particular model of decision making demonstrated by the VOTE program. According to this model a rational decision maker should should perform the following actions.

Knowledge about goal preferences and plan consequences fall outside this definition of rationality. Nevertheless viewing knowledge as a resource an agent should acquire such knowledge to increase her goals that can be achieved just as she might want to acquire more money to achieve more goals. However, it is not irrational to lack knowledge just as it is not irrational to lack money.

We suggest that our decision making model is realistic and could be extended for multi-agent interactions such as advice, persuasion, and negotiation.

Goals and Decisions

VOTE Example



Interpersonal Relationships


VOTE Program

Decision Strategies

Future Work

Qualitative Arithmetic


A Realistic Model of Rationality

Case-based Reasoning for Financial Decision Making

A Goal-Based Model Of Interpersonal Relationships

Goal-based Decision Strategies

Applying Goals and Cases to Business Decision Making

Moral reasoning

Qualitative Business Decision Making

Qualitative Decision Explanation for Information Technology Investment

An Intentional Arithmetic for Qualitative Decision Making

Slade, Automated Decision Systems

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