What is mechanistic evidence, and why do we need it for evidence-based policy?

Marchionni, Caterina; Reijula, Samuli

doi:10.1016/j.shpsa.2018.08.003

Cited by 38 publications

(30 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fig. 2 Data-driven evidence-based policy quadrants [12], [27], [30] The data-driven environment in Figure 2 is divided into four quadrants. Before beginning to describe in depth every single of important quadrants, we require to define the objective of this transition.…”

Section: Resultsmentioning

confidence: 99%

“…The discourse of evidence-based policies has become very popular not only in the scientific community but also in government institutions and business organizations [15]. The unique characteristic inherent in evidence-based policy-making is prioritizing evidence metaanalysis and randomized trials compared to other evidencebased methods [27].…”

Section: B Evidence-based Decision-makingmentioning

confidence: 99%

“…Theoretically, the evidence-based policy is separated into two main categories, namely: mechanistic evidence and statistical evidence [27]. Mechanistic evidence is evidence that connects facts to the results of analysis by producing a definite hierarchy of evidence.…”

Section: B Evidence-based Decision-makingmentioning

confidence: 99%

See 2 more Smart Citations

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Luthfi¹,

Janssen²

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Over the last decade, more and more data are collected and opened. Governments actively stimulate the opening of data to increase citizen engagement to support policy-making processes. Evidence-based policy-making is the situation whereby decisions made are based on factual data. The common expectation is that releasing data will result in evidence-based decision-making and more trust in government decisions. This study aims to provide insight into how evidence-based policy based on open data can result into uncertainty and even polarize the policy-making process. We analyze a case study in which traffic and road utilization datasets are used and model the decision-making process using the Business Process Model and Notation (BPMN). The BPMN model shows how the government and business organizations can use the data and give different interpretations. Data-driven decision-making might potentially create uncertainty, polarization, and less trust in decisions as stakeholders can give different meanings to the data and arrive at different outcomes. In contrast to the common belief, we found that the more data released, the more discussions happened about what is desired according to the data. The various directions derived from the data can even polarize decisionmaking. In other words, the more data opened, the more people can construct their perception of reality. For further research, we recommend understanding the types and role of data to create an evidence-based approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: B Evidence-based Decision-makingmentioning

confidence: 99%

See 1 more Smart Citation

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Luthfi¹,

Janssen²

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…This does not mean we cannot use probabilities to produce a causal explanation: a “mechanistic” evidence may consist either in qualitative or quantitative data (Russo and Williamson, 2007). The difference in kind between probabilistic and mechanistic evidence does not coincide with the difference in kind between two evidence-gathering methods (McKay Illari, 2011, Marchionni and Reijula, 2019).…”

Section: How Realist Policy Design Can Improve Evaluationmentioning

confidence: 99%

The contribution of policy design to realist evaluation

Fontaine

2020

Evaluation

View full text Add to dashboard Cite

The literature on policy design provides insightful resources to improve policy evaluation. We compare how this literature addresses causation, evaluation, instrumentation, and intervention, based on different methodologies. Then, we explain how the realist approach can contribute to theory-based evaluation with multi-methods. We go through the process leading from the adoption of policy aims to the production of a policy outcome by a causal mechanism linking agenda setting to policy formulation, cross-sectorial coordination, political interplays, and this policy’s outcome. This hypothesis is then tested against a typology of policy instruments classified by the State resources. These instruments are hereby treated as expected empirical observations that provide an empirical basis to assess the theoretical process following a Bayesian logic. We eventually present a five-step protocol aimed at making realist policy evaluation easier, which includes process theorization and operationalization, empirical tests design, case selection and justification, congruence analysis, and deep within-case study.

show abstract

“…Self-nudging implies not only learning a "trick"; it also requires some insight into the psychological mechanisms behind it (cf. Marchionni and Reijula 2019). To this end, laypeople need to be let in on behavioral scientists' privileged knowledge of nudging and of how the choice architecture is assumed to function.…”

Section: How To Communicate Self-nudgesmentioning

confidence: 99%

Self-nudging and the citizen choice architect

Reijula¹,

Hertwig²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

This article argues that nudges can often be turned into self-nudges: empowering interventions that enable people to design and structure their own decision environments—that is, to act as citizen choice architects. Self-nudging applies insights from behavioral science in a way that is practicable and cost-effective but that sidesteps concerns about paternalism or manipulation. It has the potential to expand the scope of application of behavioral insights from the public to the personal sphere (e.g., homes, offices, families). It is a tool for reducing failures of self-control and enhancing personal autonomy; specifically, self-nudging can mean designing one’s proximate choice architecture to alleviate the effects of self-control problems, engaging in education to understand the nature and causes of self-control problems, and employing simple educational nudges to improve goal attainment in various domains. It can even mean self-paternalistic interventions such as winnowing down one’s choice set by, for instance, removing options. Policy makers could promote self-nudging by sharing knowledge about nudges and how they work. The ultimate goal of the self-nudging approach is to enable citizen choice architects’ efficient self-governance, where reasonable, and the self-determined arbitration of conflicts between their mutually exclusive goals and preferences.

show abstract

What is mechanistic evidence, and why do we need it for evidence-based policy?

Cited by 38 publications

References 41 publications

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

The contribution of policy design to realist evaluation

Self-nudging and the citizen choice architect

Contact Info

Product

Resources

About