Annotated Bibliography On Using IT To Model Behavior For P ✓ Solved

ANNOTATED BIBLIOGRAPHY ON USE OF IT TO MODEL BEHAVIOUR FOR POLICY

This annotated bibliography focuses on the use of IT to model behavior for policy making. It discusses various scholarly articles that explore how information technology (IT) can be leveraged to improve decision-making processes in various policy domains, particularly in public health, energy consumption, and tobacco control.

1. Atkinson et al. (2015)

Atkinson, J. A., Page, A., Wells, R., Milat, A., & Wilson, A. (2015). A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implementation Science, 10(1), 26.

This article emphasizes the application of IT in public health policy-making. The authors describe analytical tools that facilitate problem-solving in health challenges. By identifying and prioritizing disease risk factors, the research illustrates how IT influences decision-making within public health. The utilization of a systems approach is discussed as critical for identifying resources essential for optimal impact. The findings highlight the role of IT in operationalizing research evidence and fostering collaboration between stakeholders, policymakers, and researchers.

2. Calder et al. (2018)

Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C. A., Douglas, R., ... & Hinds, D. (2018). Computational modelling for decision-making: where, why, what, who and how. Royal Society open science, 5(6), 172096.

This article explores the necessity for sophisticated computational models that enhance decision-making in policy contexts. It outlines how these models allow policymakers to anticipate potential outcomes, serving as valuable tools in IT. The authors highlight the benefits of acting as idea test beds and improving understanding of complex systems across various fields including urban planning and business management. Further, the article discusses the value of computational models for evaluating policies before real-world implementation, leading to more informed decisions.

3. Gore et al. (2018)

Gore, R., Lemos, C., Shults, F. L., & Wildman, W. J. (2018). Forecasting changes in religiosity and existential security with an agent-based model. Journal of Artificial Societies and Social Simulation, 21(1).

This study examines how computational models aid in evaluating and developing policies by providing experimental frameworks in virtual environments. The authors discuss the benefits and challenges of using models in policy-making, emphasizing the importance of stakeholder engagement and communication. The text underscores the significance of ethical considerations in modeling processes and illustrates how robust models can facilitate better policy decisions through increased collaboration and understanding among stakeholders.

4. Mogles et al. (2018)

Mogles, N., Padget, J., Gabe-Thomas, E., Walker, I., & Lee, J. (2018). A computational model for designing energy behaviour change interventions. User modeling and user-adapted interaction, 28(1), 1-34.

Focusing on energy behavior change, this article proposes a computational model that integrates individual and societal factors influencing energy consumption. The authors discuss how interventions can be optimized via agent-based models that account for personal values and knowledge. This approach allows for the testing of diverse variables in a controlled environment, enhancing the understanding of energy-related behaviors and fostering effective policymaking in the energy sector.

5. Wallace et al. (2015)

Wallace, R., Geller, A., & Ogawa, V. A. (2015). Data and Implementation Needs for Computational Modeling for Tobacco Control. In Assessing the Use of Agent-Based Models for Tobacco Regulation. National Academies Press (US).

This article evaluates the effectiveness of various computational models in informing tobacco control policies. By comparing macro-level and micro-level models, the authors present strategies for improving decision-making through the integration of diverse data sources. The discussion details how computational models can accommodate varying parameters to better understand human behaviors in relation to tobacco use. The challenges faced in implementing these models and their comparative advantages are critical for developing effective tobacco regulations.

Conclusion

In summary, the articles included in this annotated bibliography collectively emphasize the significant role of IT in modeling behavior for effective policy making. Through sophisticated computational models, stakeholders can anticipate outcomes, improve decision-making processes, and address complex social issues across various domains. As the landscape of information technology evolves, the integration of these models into policy frameworks will likely prove crucial for developing evidence-based, efficient strategies that enhance public health, manage energy consumption, and guide tobacco control.

References

• Atkinson, J. A., Page, A., Wells, R., Milat, A., & Wilson, A. (2015). A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implementation Science, 10(1), 26.
• Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C. A., Douglas, R., ... & Hinds, D. (2018). Computational modelling for decision-making: where, why, what, who and how. Royal Society open science, 5(6), 172096.
• Gore, R., Lemos, C., Shults, F. L., & Wildman, W. J. (2018). Forecasting changes in religiosity and existential security with an agent-based model. Journal of Artificial Societies and Social Simulation, 21(1).
• Mogles, N., Padget, J., Gabe-Thomas, E., Walker, I., & Lee, J. (2018). A computational model for designing energy behaviour change interventions. User modeling and user-adapted interaction, 28(1), 1-34.
• Wallace, R., Geller, A., & Ogawa, V. A. (2015). Data and Implementation Needs for Computational Modeling for Tobacco Control. In Assessing the Use of Agent-Based Models for Tobacco Regulation. National Academies Press (US).