The Social Impact of Computer-Mediated Voting

Arnold B. Urken

• 1. Introduction
• 2. Electronic Voting and Options for Individuality
• 2.1 Privacy
• 2.2 Asynchronous Voting
• 2.3 Reliability
• 2.4 Decision Support
• 3. Cardinal Preferences of Four Voters for Three Alternatives
• 4. Condorcet and Copeland Scores for the Voting Scenario in Table 4
• 5. Hypothetical Vote Allocations and Collective Outcomes Under One Person, One Vote (OPOV) and Approval Voting (AV) Methods
• 6. Discussion
• References

Table 3. Cardinal Preferences of Four Voters for Three Alternatives

Condorcet and Copeland’s scoring methods are normally used as ideal measures of collective outcomes. Both measures operate on information about the order of choices in voter preference orderings. Condorcet scoring computes the number of times that each alternative is preferred to every other alternative in the preference orderings of all voters. For instance, in Table 3, since A is preferred to B once and preferred to C twice, it would have a Condorcet score of 3. Copeland scoring is an extension of the Condorcet method, a net-Condorcet score, that subtracts defeats from victories. In our scenario, B and C each defeat A twice, so A’s Copeland score would equal ( 1 – 2) + (2 – 1) = 0.

This arithmetic leads to the following results:

Table 4 – Condorcet and Copeland Scores for the Voting Scenario in Table 4

These data indicate that these measures of consensus can be inconsistent with each other in subtle ways. For while Condorcet and Copeland scoring both identify B as the strongest choice, the methods provide different pictures of the strength of the consensus. A and C are tied under both systems, but the strength of the consensus is stronger under Copeland scoring than it is under the Condorcet method. The second system suggests that the collective preference intervals between the winner and A and C are 2 and 3. Under the results produced by the first method, these intervals are 3 and 6, respectively. Although these scoring methods frequently yield consistent results, they do not necessarily provide a straightforward way of indicating which voting system is best.

But the problem of choosing a voting system can be interpreted as a process of pooling information to resolve conflicts. From this viewpoint, techniques such as Condorcet and Copeland scoring can help groups clarify the nature and strength of “consensus” by deliberating about which type of collective outcome best fits their judgment. For instance, voters might consider if the intervals between the candidates are best approximated by Condorcet or Copeland scoring (or neither!).

Although using voting as a decision support system for conflict resolution may be limited by factors such as group size, decision tasks, and caliber of the decision makers, there are several other possible forms of decision support (Urken, 1988).

In administrative decision tasks, where there is a consensus on social and decision objectives, voters might rely on support systems to determine the “best” way of representing voting information. This support might provide feedback for group deliberation (or individual deliberation if a leader or supervisor is trying to pool information) or artificial intelligence or expert systems might be devised to select a voting system for voters based on a database of information about voter performance or background. This latter possibility might become very useful when many decisions must be made in a short period of time.

Another potential tool for dealing with time constraints involves looking at asynchronous decisions filtered through different voting systems to determine when a consensus can be declared to exist. For instance, in the voting scenario in Tables 1 and 2, if voters I, II, and III have already voted, a consensus cannot be determined under one person, one vote voting because a tie exists. Under the same system, if voters I, III, and IV vote first, a plurality outcome cannot be declared because if voter II votes for A, a tie would be created. Similarly, under approval voting, neither of these hypothetical asynchronous patterns of voting would reveal a consensus before all individuals have allocated their votes. When voters I, II, and III vote first, voter IV would create a three-way tie by casting an approval vote only for C. And when the votes of I, III, and IV arrive first, voter II can either make B a plurality and majority winner or, by voting only for A and C, produce a three-way tie.

Table 5. Hypothetical Vote Allocations and Collective Outcomes Under One Person, One Vote (OPOV) and Approval Voting (AV) Methods

However the scenario represented in Table 5 contains two examples of how decision support can be used to identify a consensus before all votes have been cast. Under one person, one vote voting, if voters I, II, and IV act first, then a plurality or majority consensus can be declared even though voter III has not voted. Similarly, under approval voting, if votes arrive first from I, III, and IV, B can be identified as a plurality and majority winner.

Similar decision support possibilities can be developed that combine the analysis of preference and voting structures with factors such as competence. Here the measure of competence must be well defined and related to carrying out a decision task.

A more complex form of support could be developed to guide voters operating under a “fungible” voting system (Coleman, 1973, Urken and Akhand, 1976, Urken, 1988). This method allows individuals to save and trade votes the way they currently allocate money. In this type of environment, guidance could be provided on market conditions, risks, and intermediaries, entrepreneurial interest groups and coalitions that collect votes to influence collective outcomes. Although this type of system was originally proposed as a solution to the problem of balancing intensities of preference in public policymaking, it seems more likely that online groups or corporations would be first to experiment with Computer-Mediated fungible voting to facilitate internal bargaining and negotiation (Urken and Akhand, 1976, Urken, 1988).

6. Discussion

Computer-Mediated voting is not a panacea for resolving conflicts in small or large groups of people, but people may find new ways of expressing their individuality via voting using this technology. As individuals experience Computer-Mediated voting and recognize possibilities for looking at the social function of voting in different ways, there will be gradual, piecemeal changes in social practices. Some of these changes may be quite dramatic, spurred by public attention given to certain problems and accomplishments. As news about the potentially positive Computer-Mediated voting spreads, theoretical possibilities will be refined in practice.

Stevens Institute of Technology

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