1) Values in Computational Models Revalued Computational models are mathematical representations that are designed to study the behaviour of complex systems. Systems under study are usually nonlinear and complex to the extent that conventional analytics cannot be used. Scholars have tried to establish the role played by trust and values in the use of such models in the analysis of public administration. 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? References 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.

1. 1) Values in Computational Models Revalued
Computational models are mathematical representations that are
designed to study the behaviour of complex systems. Systems
under study are usually nonlinear and complex to the extent that
conventional analytics cannot be used. Scholars have tried to
establish the role played by trust and values in the use of such
models in the analysis of public administration.
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

2. context?
References
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

3. 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?