The impact of information and communication technologies on the costs of democracy

Electronic Commerce Research and Applications 12 (2013) 440–448

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The impact of information and communication technologies on the costs of democracy Roumen Vragov ⇑, Nanda Kumar Mount Saint Mary College, United States Baruch College, City University of New York, United States

a r t i c l e

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Article history: Received 6 June 2013 Received in revised form 14 June 2013 Accepted 14 June 2013 Available online 25 June 2013 Keywords: Constitutional economics Electronic government External costs Information and communication technologies Multi-lateral communication Negotiation Shareholder democracy Voting

a b s t r a c t Recent developments in information and communication technologies (ICTs) can change the way strategic organizational decisions are negotiated and voted upon. We propose an analytical framework that investigates the effect of such technologies on the costs of the decision-making process in organizations by incorporating a technology variable in an already well-known model of public decision-making. The framework is applicable to any large group decision setting where collective decisions are made by voting, such as shareholders’ meetings, local and federal government meetings. We apply the framework to derive policies that will help to decrease the costs related to the democratic process. The first policy requires that governments should invest more in technologies that support multi-lateral communication and negotiation among citizens, and the second requires that citizens should use better voting mechanisms than simple majority voting to make decisions. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Many of our daily transactions require efficient multilateral communication and agreement. For example, communication involved in democratic political decision-making at the local or federal level is complex and almost always multilateral (Lourenco and Costa 2007). Other similar multilateral modes of communication involve employees within or across business organizations working towards a common objective, or shareholders deciding on a merger or CEO compensation. In addition, the impact of information and communication technologies (ICTs) in reducing the costs of business transactions in the bilateral communication setting is well known (Moon 2002), while their impact on the costs related to multilateral communication in democracy and public decisionmaking has been under investigated (Vragov and Kumar 2006). Although government initiatives that fall under the category of bilateral communications and bilateral transactions have been studied widely (e.g., Burns and Robins 2003), enabling active citizen participation through multi-lateral communication and decision-making has been an area that has been the most controversial and certainly the least developed, according to Marchionini ⇑ Corresponding author. Tel.: +1 7036234040. E-mail addresses: [email protected] (R. Vragov), [email protected] (N. Kumar). 1567-4223/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.elerap.2013.06.003

(2003, 2004). In addition, many studies of e-government have focused on adoption and engagement issues (Ebrahim and Irani 2005, Gupta et al. 2008, Chan and Pan 2008), but do not go further in analyzing and predicting the effects of adoption on democracy in general. There is no doubt that advances in ICTs have had an impact on democracy and public decision-making. While most Americans believe that technology improves democracy, historical evidence suggests this might not necessarily be the case (Bimber 2003). Recently, several scientists, including Duff (2005), have argued that ICT offers a new opportunity to engineer a just social order. Therefore, it is important to understand the mechanism through which changes in technology translate into improvements in multilateral communication, democracy, and public decision-making. Though some of the sections in this article focus on democratic public decision-making in the context of interactions between government and its citizens, this framework can be applied to other group decision-making contexts also. These include boards of directors deciding on CEO compensation, for instance. Even though we believe that most of the basic principles of governance outlined in democratic constitutions are timeless and are indeed worth preserving, we propose to reexamine the fundamentals of the multilateral communication between citizens and the government so that the processes which support it can be reengineered in the view of the new options provided by technology.

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In order to do that, we need to develop a theoretical framework that predicts how technology affects the costs of democracy. Such a framework will help us answer an important policy question: Are there optimal configurations of ICT tools that make group decisionmaking in a democracy work better? Are there any policies that can help technology improve the democratic process? To develop our framework we first look at the currently available tools provided by the government and identify a gap between what is feasible and what is available. Then we construct a theoretical model of the impact of technology on a general multilateral decision-making process by incorporating technology as a variable in the already well-known public choice framework pioneered by Buchanan (1968). The new model suggests steps that can be taken to reduce the costs of collective decisions in a democratic manner. The model predicts that increases in the use of appropriate kinds of ICT tools (e.g., tools that facilitate better deliberation and negotiation) can lead to a decrease in costs if an increase in the percentage of votes deemed necessary to accept a public decision also occurs. We discuss the implications of this prediction in view of current developments in electronic government projects. The model helps us make two policy suggestions: the first policy requires that governments should invest more in technology which supports multilateral communication and negotiation among citizens, and the second requires that citizens should use better voting mechanisms than simple majority voting to make decisions. This article makes several theoretical contributions. It presents one of the first theoretical models that explicitly links the various costs involved in multilateral communication processes required for collective decisions with the specific technologies that influence them. Our research also shows how technology can be integrated successfully as an important variable in an existing traditional model of public decision-making. In addition, the paper discusses methods to generate and evaluate hypotheses based on the analytical framework that is presented. The results of the model also have important implications for practice. Our analysis is especially useful in settings that involve large groups of people. This is because getting people together in the same location and around the same time is hard. The range of communication possibilities is narrow too. So determining the balance of various costs involved in decision-making is important. For example, it might be very limited negotiation in large groups, and simpler yes–no mechanisms for arriving at decisions. Our analysis also provides a direction for the development of future tools that increase the efficiency of public discourse through negotiation. While the article discusses a multilateral communication framework using government as the primary example, the framework developed is general; it can be used in any large group settings. The last section of the article summarizes the framework that is developed and shows how it can be used to support current political tradition to better reflect the mandate of the stakeholders involved (e.g., citizens in a government or the shareholders in a company). We then discuss ways to further augment the political processes of these entities – the government or the company – so that it involves democratic decision-making with the use of currently available ICTs.

2. Relevant literature 2.1. Motivation: the public choice framework The main thrust of government-to-citizen (G2C) initiatives thus far has focused on how best the government can provide information and services to its citizens in the digital form. These G2C initiatives typically try to mimic the offline interactions with citizens by using ICTs. For example, many governments in well-

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developed countries currently operate web sites that provide citizens access to information, various forms and templates they need to get a service, and in some cases provide even transaction services, such as the filing of taxes, the renewal of driving licenses and passports online. Most of these services support only bilateral transactions though, just like most business processes in e-commerce. While the provision of such information and services is certainly an important element in making the governments more efficient and effective in serving the interests of their citizens, the transformative possibilities afforded by ICTs can be realized only if we step back and examine the historical reasons for the existence, current organization and functioning of the government. The areas of constitutional economics and public choice provide a useful framework for analyzing different communication tools required for effective governance in terms of their costs. James Buchanan pioneered this field of research and received a Nobel Prize in 1986 for his contribution. Buchanan, as well as other social scientists who have studied the government, agree that the main purpose of the government is to provide public goods. Public goods, in contrast to private goods that are only for personal enjoyment and consumption, are broadly defined as all products and services that benefit a large group of people. Once provided, many of these public goods are non-excludable, which means that it is difficult and economically inefficient to exclude individuals from using the goods, and non-rivalrous, which indicates those goods for which consumption by one person does not prevent consumption of the same good by other. The procurement of goods with similar characteristics – for example, highways, bridges, education, healthcare and national defense – has been delegated to the government mostly because of the so-called free-rider problem. In a free market economy, every individual citizen will expect others to contribute to a public project while minimizing his or her own contribution. Hence, enough contributions may not be mustered for the procurement of this type of public good or service, even when its benefits outweigh its costs (Lipsey et al. 1993, Buchanan 1968). The costs incurred by a government when providing a public good can be divided into two broad categories: external costs and decision-making costs (Buchanan and Tullock 1962). These costs are related to the decision-making process only: they do not include the costs of providing a public service once the service has been approved. External costs are those incurred by an individual who is adversely affected by a public decision. For example, let’s say that a new public stadium is to be built in Upper Manhattan of New York City. The stadium is beneficial to many people but harmful to citizens who live close to the area. It may cut off neighborhoods, as well as contribute to a sudden influx of vehicular and pedestrian traffic. The sum of these adverse effects from a particular decision to all the affected individuals represents external costs. The size of the external costs depends on the extent to which a decision incurs damage to individuals and not on the type of decision mechanism used. The external costs are borne by the individuals whose vote did not coincide with the final outcome of the group decision process. In the above example, should the opponents of stadium win the referendum (or another suitable decision-making mechanism), then the adverse impact of that decision to the proponents of the stadium (e.g., through reduced job creation) would represent external costs. We denote by y the proportion of citizens who need to vote ‘‘yes’’ to a proposal so that a proposal is accepted. It is clear that the external costs depend on y because, the higher the y, the less people will be negatively affected by a public decision on average. The decision-making costs, on the other hand, are costs that are incurred in the process of reaching a decision. These include the cost involved in configuring the right mix of ICT tools so that relevant information can be presented, as well as appropriate tools

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provided for stakeholders to interact among each other, discuss issues, and negotiate on possible outcomes. These decision costs also include those needed for set up and maintenance of appropriate mechanisms for reaching decisions. These are not just the logistical costs involved in setting up electronic voting machines, for example, but also the costs that are incurred if users have to scroll down multiple screens and menus, and enter a vote many times through multiple iterations. The decision-making costs also depend on y because, if y is lower, then less decision-makers on average would have to engage in negotiation in order for a proposal to be accepted (Buchanan 1968). For example, if y = 1 then, on average, a large number (and sometimes all) decision-makers need to be engaged in negotiations so that a proposal is accepted by all. If y = 0.6, then it might be the case that a proposal can be accepted even if 10% of the decisionmakers do not participate at all in the negotiation process and the 60% is gathered from the remaining 90% of decision-makers. The public decision-making process tries to achieve the minimum of the sum of the two costs as shown in Fig. 1. The variable y shown on the horizontal axis in Fig. 1 indicates the proportion of voters that need to vote ‘‘yes’’ for a decision so that the decision can be approved and implemented. The first problem with this simple model is that it does not explain why most decisions (with the exception of some fundamental decisions such as right to free speech and the right to own property) are made with a simple majority voting rule, that is y > 0.5. This framework assumes implicitly that the two curves in Fig. 1 cross exactly at y = 0.5. Nevertheless this simple model can be used to predict the effects of technology on the external and decisionmaking costs. Given the definition of the two different types of costs, external and decision-making, we can infer that changes in technology will not affect the external costs but will affect decision-making costs. More specifically, the use of appropriate and better ICTs will shift the decision-making cost curve to the right, as shown in Fig. 2. This is because, for every y, arriving at a decision should be easier with better ICTs. The shift in the decision-making cost curve results in a shift in the curve that represents the sum of the two costs, which can also change the location of the minimum. Using this simple qualitative model based on Buchanan’s previous work, we can say that improvements in technology should change the y that is necessary to achieve the minimum sum of the two important costs. We have seen much improvement in technology over the last decade; however, we have not seen a change in y. To explore the

Fig. 1. Public decision-making costs. Note: The horizontal axis represents the proportion of citizens (y) who are required to vote ‘‘yes’’ to a proposal so that the proposal is accepted.

Fig. 2. The impact of technology on the decision-making costs and y.

reasons for this apparent anomaly and to develop a more complete analytical framework of the impact of technology on democracy, we will take a closer look at decision-making costs, the mix of ICT tools used in governance and the way these costs have been affected by these tools. In the next section we review the current use of technology in public decision-making and then propose a new extended analytical framework of public decision-making. 2.2. Electronic government: current state of affairs According to recent literature in constitutional economics, advances in Internet-enabled ICTs have the power to reduce external costs through negotiation and more efficient voting mechanisms, such as pivotal voting, storable voting, or voting with compensation (Attieh et al. 2000, Oprea et al. 2006), Casella et al. 2005). Some of these voting mechanisms allow decision-makers to at least partially express the intensity of their preferences. As suggested by theory, there are voting mechanisms in which it is optimal for every individual to report his or her preference truthfully (Vickery 1961, Groves 1973, Clarke 1971), although these mechanisms require additional taxation and some special assumptions about individual preferences. Versions of these mechanisms have been successfully tested in a laboratory setting with promising results (Attieh et al. 2000, Smith 1977) and have been shown to work well in small groups of individuals. The applicability of these results to large groups is still unknown. Large group tests have not been implemented mostly due to the complexity of the mechanisms – which can be reduced by the usage of new IT (Vragov and Kumar 2006). There are also some voting mechanisms that do not require truthful revelation of preferences yet they do lead to more efficient decision-making with less external cost through negotiation. This has been confirmed by an actual field test with the Public Broadcasting Cooperative and many experimental studies (Ferejohn et al. 1979, Oprea et al. 2006, Casella et al. 2005). These voting mechanisms need to be combined with some communication tools so that they can be deployed online and evaluated under more realistic conditions preferably starting on a local level. Internet-enabled ICT tools that support communication of information (from government to citizens) also have the potential to lower decision making costs as these new technologies have dramatically reduced the cost of citizen’s access to information. While access to these tools is still an issue that hinders participation of specific groups of citizens, the power of these tools can be amply illustrated by the speed with which anti-war protests were organized in the U.S. in 2003 (Zernike and Murphy 2003). In comparison, the Vietnam era protests took years to organize primarily

R. Vragov, N. Kumar / Electronic Commerce Research and Applications 12 (2013) 440–448 Table 1 Mechanisms for citizen participation. Components

Traditional ICTs (Before 2000)

Internet-enabled ICTs

Dissemination Component (DC)

TV, radio, newspapers, printed materials available in public spaces or government offices

Negotiation Component (NC)

Telephone, fax, face-to-face meetings with government representatives, town hall meetings Paper-based ballots, electronic voting systems designed for physical polling booths

Traditional + digitized information on the government web site; email distribution lists, recorded audio, video Traditional + feedback forms, chat tools, interactive discussion boards, electronic petitions, blogs Traditional + online electronic voting systems

Voting Component (VC)

due to the lack of such democratizing tools. The case studies in the OECD (2003) report also point to the fruitful use of the new ICTs in advancing a more active engagement of citizens with their government. In this section, we categorize the ICT tools under three distinct mechanisms – dissemination, negotiation and voting and compare these different mechanisms available for citizen participation before and after the advent of Internet-enabled ICTs. (See Table 1.) Looking at the table and at some reports on the current level of government service (OECD 2003), the mix of ICT tools provided by governments to enhance the three components of the public decision-making process does not fully take advantage of the new possibilities. Kumar and Vragov (2005) already conducted a more specific survey of the web sites of the fifty U.S. states. The results show that about 80% of the web sites surveyed provided recorded video or audio clips, while about 55% of the web sites surveyed provided support for email distribution lists that pushed information to the citizens’ email in-boxes. In contrast, only 8% provided support for discussion boards where citizens could congregate on-line to deliberate various issues. Applying the classification in Table 1, we can say that 85.1% of the web sites surveyed provide support for DC tools that facilitate one-way communication from the government to its citizens. On the other hand, only 17% of the web sites provide NC tools that promote active participation and deliberation by citizens. Given the challenges associated with providing online voting support, not surprisingly, none of the web sites provided support for VC tools. In general, while costs related to the dissemination of information have dramatically decreased, there is currently not enough support provided for multi-lateral negotiation by the government in any cohesive manner. The lack of support for discussion boards is especially surprising as the asynchronous discussion boards can be designed to foster discussion and negotiations through multilateral communication. They can also be effectively used as repositories of civic knowledge. The contents of these discussion boards are primarily driven by citizens, and are above all eminently scalable (as opposed to providing live help on issues that have been experienced by other citizens). Hence, they cost little to set up and maintain as avenues of active citizen engagement. In contrast to DC and NC, perhaps not surprisingly, there was literally zero support for VC. This can be due to many reasons. The use of electronic voting mechanisms to enable citizens to make decisions on public goods is faced with many applicability problems: digital divide, security, privacy and others (Vragov and Kumar 2006). It was also not considered a viable option due to the relatively smaller number of appropriate types of voting mechanisms (on account of relatively recent history of this particular stream of research) and more critically, the prohibitive implementation obstacles until the advent of Internet-enabled ICTs.

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In his book, The Demand and Supply of Public Goods, Buchanan (1968) shows that public goods by nature are more complex commodities than purely private goods. Private goods can be traded efficiently in relatively simple markets while public goods require some public decision-making rule in addition to a market. He agrees that unanimity in making decisions about which public goods should be provided and how they should be financed can be one of these rules because it guarantees that no one will be hurt by the collective decision. However, he suggests that eliciting unanimous approval might be prohibitively costly. He mentions that 50% + 1 majority rule for making decisions can be a good alternative provided that the same minority is not consistently hurt in all decisions. Buchanan suggested also that trading of votes between individuals for different decisions will ensure an efficiency level close to that under unanimity rule. Here, we note another possible formulation of an electronic market for public goods – one where the trading commodity is votes instead of goods and services. Some of Wicksell’s and Buchanan’s ideas were developed further with more formal mathematical treatments and proofs. Clarke (1971) described a multi-part pricing mechanism that provides an incentive to citizens to reveal their value for a public project and assigns its citizen a cost share in such a way so that costs are covered. Groves (1973) created a mathematical model of transactions within any kind of a multi-person organization. He was concerned with providing a mechanism that will induce members of an organization to behave as if they have formed a team. A team decision problem is a multi-person joint decision problem in which the decision-makers base their decision choices on different information, yet are motivated by a common goal. One of his results demonstrated that it is possible to create a market-like mechanism of exchange and compensation between individuals that will achieve the most efficient allocation of resources and will make the best decisions. In a later article, Groves and Ledyard (1977) extended these results as they apply specifically to the Government and the way public goods are chosen and financed. Vickery (1961) also described a mechanism for solving the market monopoly problem. The same mechanism could be applied to a public good. A small, more recent literature has developed around the compensation mechanism introduced by Varian (1994). Varian’s compensation mechanism is a procedure for inducing decision-makers in small groups to make socially-efficient decisions. The problem with this mechanism is that it relies, critically, on the standard theoretical assumption that all participants know the values and costs of all other participants. Simplified versions of more specific mechanisms based on the reported theory above have been tested in laboratory and field experiments. Many of these mechanisms are still very abstract in terms of their application to decisions that can be made within the current political tradition. For a survey of important studies that have implications for the design of voting mechanisms, see Vragov and Kumar (2006). The results reported in the survey demonstrate that electronic market mechanisms can play an important part in decisions related to the allocation, financing, and distribution of public goods. The survey also discussed some of the salient problems in the implementation of these voting mechanisms such as complexity, scalability and information overload and outlines how the new ICTs help to mitigate these problems. In order to investigate different ways of using ICTs to decrease the costs of the political process, and improve government functions and citizen satisfaction, we constructed a specific and welldefined analytical framework of democratic decision-making. The model separates the costs related to public decision-making into three categories, as in Table 1, and discusses ways in which usage of technology can affect the costs in each category.

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Table 2 Model notation. Variable

Explanation

D F(x) h G(h) y d c n e W(y) ic, id, in

Number of proposals to be considered Individual value distribution with support [a, b] Proportion of individuals who vote ‘‘yes’’ on a proposal Probability density function of h Proportion required for acceptance of a proposal Cost of discovering one’s value Cost of voting Cost of negotiations External costs Welfare function Variables that express the effect of technology on the cost of voting, the cost of discovering one’s value, and the cost of negotiations respectively. The value of y at which W(y) achieves its minimum

y*

3. Group decision-making: a more inclusive theoretical framework Suppose that a group of individuals has to decide on a series of important issues related to their common social well-being. The individuals have agreed to entertain and vote on proposals that deal with these issues in a binary fashion using a referendum until an agreement is reached. At any time there are D proposals that have to be considered.1 We assume that the proportion of individuals that participate in the decision-making process is a fixed parameter that stays constant in the rest of the model. We further assume that each proposal has some consequences for every participating individual. Also, every participating individual can estimate these consequences in terms of dollars, and we call the resulting number the individual’s value for the specific proposal. Let us mark by d the sum of the individual costs related to estimating the individual’s value. (All notation is explained in Table 2.) It is clear that this sum is part of the costs related to the public decision-making process but does not depend on y, the proportion of votes required to accept a decision. An individual’s value for a proposal can be positive or negative. Let us assume for simplicity that all values for a proposal follow some cumulative distribution F(x) with support [a, b] (a, b > 0), which expresses the proportion of individuals with values between a and x, so that the population size is normalized to 1. An individual will vote ‘‘yes’’ for a proposal if his or her value for the proposal is positive, and ‘‘no’’ if his or her value for the proposal is negative. Suppose that an initial proposal is generated randomly. We signify by h the random variable representing the proportion of individuals who will vote ‘‘yes’’ for a proposal (0 P h P 1). Let G(.) be the cumulative distribution function of h. The probability that a proposition will be accepted is Pr[h > y] = 1  G(y) and the expected number of proposals to be accepted is D[1  G(y)]. If we look at this decision paradigm as representing a political process, we can say that, for reasons of political stability, we need to have y > 1/2. This is because, from a political science viewpoint, a decision could be successfully enforced if at least half of the population agrees with it. If we want to maximize the number of decisions that are accepted, we have to choose the lowest possible y, which is 0.5 plus one citizen. This is simple majority voting (Mueller 2003). We will see below that simple majority voting does not necessarily minimize the costs related to the decision-making process, as has already been confirmed by previous theoretical models (Buchanan and Tullock 1962). 1 The first obvious impact of ICTs on the decision-making process is the possible increase in D – the number of proposals that can be considered for a fixed period of time. Since in this model we are focusing on the costs involved in each decision, we decided to treat D as an exogenous variable. Thus our analysis applies to any value of D. We discuss the implication of this assumption in Section 7.

With experience, individuals may find that sometimes they can change the outcome of a decision that a proposal be rejected with the help of negotiations. For example, suppose that there is a proposition p, which is characterized by a value distribution F, as defined above. Suppose that initially F(0) > y. If the referendum is conducted without negotiation, the majority will vote ‘‘no’’ and this specific proposal will be rejected. However, individuals can engage in negotiation to change the initial distribution. Let us mark the cost of these negotiations by n(y). Negotiation can take two different forms: the text of the proposal can be altered to accommodate a larger percentage of the individuals or individuals who favor the proposal can compensate in some way individuals who do not favor the proposal.2 The end result is that the value distribution F is transformed into a new distribution F1, for which F1(0) > y and the new or revised version of the proposal is accepted. In this way, individuals can decrease the total cost of a public decision. Let c be the cost of voting in one referendum for all individuals. This involves the cost of the effort to submit one’s vote and the cost of conducting a referendum. This cost also does not depend on y but it also is a part of the total costs of the public decision-making process. Having this in mind, we can say that the total costs related to the public decision-making process is d + c + n(y). We should not forget to provide an expression for the external costs of the agreement, which will depend on the final distribution of the individuals’ values and y. The external costs can be expressed as:

8 R < F 1 ð0Þ 0 xf1 ðxÞdx if F 1 ð0Þ P y a1 eðyÞ ¼ : ½1  F ð0Þ R b1 xf ðxÞdx if F ð0Þ < y 1 1 1 0 Then, the total cost related to the achievement of agreement is d + c + n(y) + e(y). We can set the welfare function here equal to the total cost of the decision-making process or W(y) = d + c + n(y) + e(y). We then can define minimizing W(y) as the main objective of the decision-making process: min W(y) = min [d + c + n(y)+e(y)]. The solution of the minimization problem, y*, has to satisfy the first order conditions or n0 (y*) + e0 (y*) = 0. Notice that costs marked by d, c and n(y) might be easier to observe while F and F1 are unknown functions because the individual values are private information. This is why the above optimization can never be carried out in practice. The new expanded model is displayed graphically in Fig. 3. The model is very similar to the one in Fig. 1. We have only added two more cost components (c and d) and we use n(y) as a narrower definition of decision-making costs that are a function of y. We next provide an example to show that there are cases when setting y = ½ does not minimize the costs related to the process of achieving agreement – it only maximizes the number of decisions reached within a certain period of time. Also if the value density f is skewed, external costs can be very high when y = ½. Example 1. Fig. 4 displays a particular f(x) density with a = 10 and b = 0.5. Let us suppose for the sake of this example that there are only a few more individuals on the positive side than on the negative side, so that during simple majority voting the proposition is accepted. The total benefit to the people who voted for the proposition is approximately equal to 1=4 , while the external costs 2 Negotiations in our model are an instrument for redistribution or changing the proposal to be voted on so that external costs are decreased. For example, suppose the values for a proposal of three individuals are $3, $4, and -$10. Given majority rule, the last individual can negotiate a transfer of $3.5 to the first individual if she agrees to vote against the proposal. Without negotiation, the external cost is $10 and with the mentioned negotiation and agreement for transfer the external cost is $4. The individuals can also work towards changing the proposal in such a way so that the new values are $3, $5, and $0.50 without transfers, and in that case external costs are 0.

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to a new uniform distribution with a1 = 0.2, b1 = 0.7. Then F1(0) = 0.22. The external costs will be 0.22  0.1 = 0.022. We see that in our example, as a result of a 0.01 increase in negotiation cost, we achieved a 0.041 decrease in external costs. Therefore, we obtain a net gain of 0.031.

4. Implications: the impact of ICT

Fig. 3. New expanded model of the costs related to the public decision-making process.

Using the new theoretical model described above, we can better predict how changes in any of the three components in Table 1 will affect the cost of the public decision-making process. Our model suggests that the usage of new ICTs can impact these costs in several different ways. More formally, let us introduce i as a variable that represents the level of technology. In addition, let us define id, ic, and in, as the impact of technologies specifically affecting the costs marked by d, c, and n(y) in the above model. We can now see how the introduction of such technologies will influence our welfare function W(y). Also let us mark by y* the minimum of W(y) before new technology is introduced. 5. The impact of id on W(y)

Fig. 4. A skewed probability density function f(x) of individual values.

are much higher. Using the formula mentioned above the external costs are approximately 11=4 . This result is not new. It mirrors previous discussions in the public choice literature. The new formulation here, however, helps us get a closer look at how the different types of costs are influenced by technology. Generally, we can guarantee 0 external costs only if y = 1. Making y higher than ½ will most likely lead to a decrease in the number of decisions reached within a certain period of time unless this decrease is offset by an increase in negotiation. We can also see that instead of trying to maximize the number of decisions to be completed within a certain period of time, we can focus on minimizing the costs of the process. Negotiation is the key to decreasing external costs. It also is helpful for finding the minimum of the costs of the decision process irrespective of the value of y. Let us investigate this further with the help of another example.

The process of value estimation is guided mostly by the dissemination component (DC) since decision-makers (voters) need information about a proposal in order to determine how to vote. This means that investing in improvements of the dissemination component should result in individuals having a better understanding of the consequences of their decisions and, therefore, in a decrease in d. We express this mathematically in the following way: dnew = d  id. We can rewrite our new welfare function as W(y) = c + d  id + n(y) + e(y). Since n(y) and e(y) are not affected, the optimality conditions are the same, and W(y) achieves a minimum at the same value of y as before, so ynew = y*, and W(ynew) = W(y*)  id. This means that improvements in the DC component decrease the cost of the decision process but do not affect y. See Fig. 6. 5.1. The impact of ic on W(y) Clearly, the c cost related to VC can also be decreased substantially with technology when citizens are provided better and cheaper access to voting sites on or offline. We express this mathematically in the following way: cnew = c  ic. We can rewrite

Example 2. Suppose that f is a uniform density with a = 0.5 and b = 1. F(0) = 0.33 < 0.5. (See Fig. 5.) Any y < 0.67 will result in a decision without negotiation. The external costs will be 1/ 12 = 0.083. Suppose that we can spend 0.01 on negotiation to get

Fig. 5. Shift in the density f due to successful negotiations.

Fig. 6. Improvements in DC result in a decrease in d, decreased uncertainty of e, and decrease in total costs, but do not result in a change of the optimal y.

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our new welfare function as W(y) = c  ic + d + n(y) + e(y). Since n(y) and e(y) are not affected, the optimality conditions are the same, and W(y) achieves a minimum at the same value of y as before, so ynew = y*, and W(ynew) = W(y*)  ic. This means that investing in the voting component will decrease the total cost of the public decision-making process but will not change the optimal value of y. (See Fig. 7.)

5.2. The impact of in on W(y) Improvements in NC can have two effects, They can affect n(y) directly in a similar fashion as the other two costs mentioned above. We express this mathematically in the following way: n(y)new = n(y)  in. Improvements in NC, can also affect e(y) indirectly as discussed in Example 2 of Section 3. The effect here is not a simple linear effect. This is because e(1) = 0, irrespective of the size of the technology improvement. Thus, in affects the slope of e(y). One way to model this is e(y)new = e(y)/in. Then, our new welfare function is W(y) = d + c + n(y)  in + e(y)/in, and we have a new minimum ynew which is a solution of n0 (y) + e0 (y)/in = 0. The value of Wðynew < Wðy Þ  in . This illustrates the most important implication of this model. The result here suggests that technology can change the negotiation costs directly but also the external cost indirectly and also the value of y at which the minimum is achieved. (See Fig. 8.) Past research has shown that electronic markets, databases, supply chain management solutions and others have decreased the cost of bilateral negotiations among businesses (Kumar and Peng 2006). This leads us to believe that technology can similarly decrease the cost of multilateral communication in negotiations. For example, with the use of modern technology, a $1 increase in negotiation cost in many cases might lead to a change in the value distribution F1 and a $2 decrease in external costs beyond that point. This means that the use of technology in negotiation might result in much lower total costs of the group decision-making process. Hence, the government and the public should invest more in usage of technology that supports negotiation. Since we do not know the shape of the f function, we would not know which decisions will benefit from further negotiation. However, to stimulate negotiation in the presence of new technology, we can increase the value of y for every decision.

Fig. 7. Improvements in VC result in a decrease in c, and decrease in total costs, but do not result in a change in the optimal y.

Fig. 8. Improvements in NC result in a decrease in n, a possible decrease in e, a decrease in total costs, and, in some cases, a change in the optimal y.

The basic model outlined above also explains why the optimal value of y might not have been affected by changes in technology over the last decade. As demonstrated in one of the previous sections, most digital government projects have been focused on improving the DC and Fig. 6 suggests that changes in DC affect the total costs but do not change the optimal y. Figs. 6–8 actually represent specific model hypotheses that can be tested directly in the experimental laboratory or the field, however, we leave this endeavor to future research.

6. Discussion ICT has been influencing the formation of associations and politics in general, even though this has not been directly facilitated by governments (Jenson et al. 2006) and despite the absence of clear valid methodologies for the use of web-based group decision support systems (French 2007). It is interesting to note that private and non-profit organizations have been quicker to realize the costsaving potential of negotiation tools and are therefore much ahead of the government in providing support for them. We already mentioned that one cheap and easily scalable tool that can be used to support multilateral communication in negotiations is the bulletin board. Bodies Electric and Votia are examples of two private companies that specialize in developing and integrating ICTs to support government decision-making over the Internet including support for bulletin boards. Certain types of electronic markets can also be used in place of negotiation and voting. One such tool has been used by the Public Broadcasting Corporation to negotiate and vote on TV programs that should be funded. The mechanism resembles an electronic market in which TV stations bid in an iterative manner until they reach a common decision suitable for every station. For more details, see Ferejohn and Noll (1976). It will be interesting to evaluate these tools in view of the model discussed above. Since technology tools have the potential to change the optimal value of y, they can lead to increased demands by the public to change established voting rules, such as simple majority voting. In some cases this might require constitutional amendments. Our model is one of the first to show exactly how improvements in technology can lead to major changes in some of the established rules of democracy. The model can serve to explain partially how technology can support a peaceful transition from thin democracies to strong

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democracies. Benjamin Barber (1985) describes how most major democracies employ a mixture of executive, legal and legislative arms as well as elaborate checks and balances to provide governance to its citizens (Shapiro 2003, Zakaria 2004). While such systems have yielded functioning democracies, they are also prone to distortions by special interests. Barber (1985) calls these types of democracies thin democracies, as they possess very limited avenues of action for citizens to express their preferences, for example, by voting periodically for electing representatives at various levels as well as occasionally voting on referendums. Barber envisions a democracy that involves a more engaged citizenry making decisions that respects not only the public interest but also individual preferences and calls it a strong democracy. Respect for individual preferences is crucial as that is what would differentiate a strong democracy from other forms of democracy such as mob rule (or even fascism or totalitarianism masquerading as democracy in the name of common good) when the emphasis is on engaged citizenry. Providing more options in terms of negotiation and voting mechanisms (other than simple ‘‘yes’’ or ‘‘no’’ voting) could increase citizen engagement into the political process and provide more opportunities for every citizen to voice his or her opinion. Academic research on the development of new negotiation tools is also currently on the way (Phang and Kankanhalli 2005). Vragov and Kumar (2006) examine the various dimensions of the complexity of potentially useful electronic market mechanisms that involve negotiation for making decisions on public goods, and offer specific suggestions for actionable further research to alleviate these concerns. The Center for Deliberative Democracy at Stanford University uses a variety of ICTs to inform citizens about policies and then track how opinions change after deliberation, and researchers at George Mason University and Baruch College are currently engaged in large-scale testing of the applicability of three electronic voting and negotiation mechanisms that have been proposed in previous literature: fractional voting, pivotal voting, and voting with compensation.

7. Conclusion This article uses past research in the social sciences to demonstrate the potential of new ICTs to reduce the costs of the decisionmaking process. We demonstrated that governments in general have so far ignored the potential of ICTs to increase the efficiency of multilateral communication required for group decision-making. Most current e-government initiatives are predominantly geared towards supporting one-way communication from the government to the web site, rather than enabling citizens to make connections and deliberate issues using the power of Internet-based ICTs. The lack of support for discussion boards is especially surprising as they can foster deliberation and be used as repositories of civic knowledge. Their contents are primarily driven by citizens, cost little to set up and maintain, and are above all eminently scalable – as opposed to providing live help on issues that have been experienced by other citizens. Using a theoretical model of the impact of technology on decision-making under democratic regimes, this article identifies a specific area in which technology can be applied to visibly increase benefits to participating citizens. Our model suggests that the government should concentrate efforts on providing more tools for multilateral communication that will foster more negotiation and discussion among citizens. This conclusion is supported by other observations related to group decision support systems presented in French (2007). We found that, after these tools are provided, the proportion of people that need to vote ‘‘yes’’ before a decision is accepted should be increased to levels higher than 50% +1. These conclusions can easily be applied to other group decision-making

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settings such as those within business teams or shareholder meetings. Our analytical framework is based on the behavior of rational actors, so it does not account for purely emotional or irrational factors. We also expect results in practice to be influenced by the power of current bureaucracies, power struggles between influential interest groups, general resistance to change, and corruption within the circles of public agencies. The model also assumes random generation of proposals and smooth cost functions for mathematical simplicity. Future models can extend the results to more realistic functional forms and could include analysis of the process of generating proposals. The inherent difficulties involved in estimating external costs might seems as a drawback at first sight, however, experimental methodologies can simulate external costs and thus can help to investigate different scenarios before various negotiation and voting mechanisms are implemented in practice. Another interesting issue to explore further is whether the appropriate use of ICT components – specifically deliberation – and voting mechanisms could pave the way to strong democracies with a more engaged citizenry actively participating in the legislative branch of the government, and whether this transition can be accomplished in an incremental way within the existing framework of governance. Referenda and elections where citizens vote periodically, as well as more complex voting and negotiations in which the elected representatives engage to pass bills, are examples of situations where appropriate electronic market mechanisms could be introduced to enable better and more efficient decision-making. In the future we will search for even better multilateral communication protocols that decision-makers can use to describe and exchange information about each others’ preferences and then to efficiently translate this information into a decision acceptable to all. References Attieh, G., Franciosi, R., Mark Isaac, R., 2000. Experiments with the pivot process for providing public goods. Public Choice 102 (1–2), 93. Barber, B., 1985. Strong Democracy: Participatory Politics for a New Age. University of California Press, Los Angeles, CA. Bimber, B., 2003. Information and American Democracy: Technology in the Evolution of Political Power. Cambridge University Press, New York, NY. Buchanan, J., 1968. The Demand and Supply of Public Goods. Rand McNally, New York, NY. Buchanan, J.M., Tullock, G., 1962. The Calculus of Consent: Logical Foundations of Constitutional Democracy. University of Michigan Press, Ann Arbor, MI. Burns, J., Robins, G., 2003. Moving towards e-government: a case study of organizational change processes. Logistics Information Management 16 (1), 25–35. Casella, A., Palfrey, T., Riezman, R., 2005. Minorities and Storable Votes. Working paper. Columbia University, New York, NY. Chan, C., Pan, S., 2008. User engagement in e-government systems implementation: a comparative case study of two singaporean e-government initiatives. Journal of Strategic Information Systems 17 (2), 124–139. Clarke, E., 1971. Multipart pricing of public good. Public Choice 11, 17–33. Duff, A., 2005. Social engineering in the information age. The Information Society 21 (1), 67–71. Ebrahim, Z., Irani, Z., 2005. E-government adoption: architecture and barriers. Business Process Management Journal 11 (5), 589–602. Ferejohn, J., Noll, R., 1976. An experimental market for public goods: the PBS program cooperative. American Economic Review Papers and Proceedings, 267– 273. French, S., 2007. Web-enabled strategic GDSS, e-democracy, and Arrow’s theorem: a Bayesian perspective. Decision Support Systems 43 (4), 1476–1484. Groves, T., 1973. Incentives in teams. Econometrica 41, 617–633. Groves, T., Ledyard, J., 1977. Optimal Allocation of Public Goods: A Solution to the ‘Free-Rider’ Problem. Econometrica 45, 783–809. Gupta, B., Dasgupta, S., Gupta, A., 2008. Adoption of ICT in a government organization in a developing country: an empirical study. The Journal of Strategic Information Systems 17 (2), 140–154. Jenson, M.J., Danziger, J.N., Venkatesh, A., 2006. Civil society and cyber society: the role of the internet in community associations and democratic politics. The Information Society 23 (1), 39–50. Kumar, N., Peng, C., 2006. Strategic alliances in e-government procurement. International Journal of Electronic Business 4 (2), 136–145.

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