Public decision-making is itself a complex endeavour that involves the input of multiple stakeholders. Usually, there are a lot of conflicting interests that influence the final outcome of such decision-making processes (Klabunde & Willekens, 2016). In a computational model, a number of factors equally influence the outcome of the process. One of them is the number of actors involved –the presence of more actors normally implies increased mistrust. Another factor is the amount of trust that already exists among the decision makers. In cases where the group is homogenous, there is likely to be more trust and thus, less concern about the number of actors involved.
Given the importance of these two factors, the designer of any such model bears the largest burden in assuring the value of the model. He or she can choose to implement agency by humans or by technology depending on the number of actors and trust among them. Also, model designer determines the margins of error from each scenario while modelling (Gershman, Markman & Otto, 2014). Since in conventional decision-making processes different actors have different roles, the model designer may decide to accord different levels of authority to different actors. Nevertheless, they must ensure that such a decision does not affect the trust of the system. Overall, what values are sought from a computational model in a public decision-making context?
Gershman, S. J., Markman, A. B., & Otto, A. R. (2014). Retrospective revaluation in sequential decision making: A tale of two systems. Journal of Experimental Psychology: General, 143(1), 182-194.
Klabunde, A., & Willekens, F. (2016). Decision-making in agent-based models of migration: state of the art and challenges. European Journal of Population, 32(1), 73-97.
2) Active and Passive Crowdsourcing in Government
The authors of the article “Active and Passive Crowdsourcing in Government” discuss the application of the idea of crowdsourcing by public agencies. It leverages Web-based platforms to gather information from a large number of individuals for solving intricate problems (Loukis and Charalabidis 284). The scholars revealed that the concept of crowdsourcing was first adopted by organizations in the private sector, especially creative and design firms. Later on, state agencies began to determine how to leverage crowdsourcing to obtain “collective wisdom” from citizens aimed at informing the formulation and implementation of public policies.
Active and passive approaches to crowdsourcing are similar as they are both directed at creating innovative solutions based on the knowledge, thoughts, and insights of members of the public. They are highly automated and contain application programming interfaces (API) (Loukis and Charalabidis 264). In addition, the two models involve the use of Web 2.0 technologies such as blogs, online forums, and news sharing sites for data collection. However, they differ in their ICT infrastructure. Active crowdsourcing platform has a task management function for adding multimedia and starting campaigns. It also contains a contribution management component for processing content from citizens. Conversely, passive crowdsourcing platform does not have functions for analyzing information from users.
The researchers assert that the crowdsourcing methods they recommend for state agencies should be aligned with the special needs of users (Loukis and Charalabidis 261). They conducted extensive research to indicate that public institutions should improve the application of process models in gathering information from citizens. Notably, the government is still experimenting on the use of the crowdsourcing methods that are discussed in the article. Consequently, it is necessary to ask the following question: In what ways can the government realize the real-life application of crowdsourcing models?