SMART MODEL-BASED GOVERNANCE: Achieving Effective Decision Making through the Synergy of Data-Based Technology with Modeling & Simulation Approaches

Dear Colleagues,

We invite submissions of papers to this Special Issue, that seeks to explore and contribute to the intersection of research fields such as governance, decision-making, modeling and simulation (different paradigms), and data analytics (artificial intelligence, machine learning, analytics, etc.). By joining together theories, methods, practices, and tools from such fields, novel approaches shall be explored that aim to make several decisions in a short time interval while coping with an extensive amount of data, hence addressing both current and future challenges in the process of improving the overall outcomes of good governance.

The specific concept of governance has been widely discussed in recent decades, but there is still no consensus on its definition. Hufty (2011) argues that it is one of the fuzziest concepts currently in use. However, “governance” is not a recent term. Some of its definitions related to institutional structures emerged back in the 18th century, and since then, it has been used in many distinct contexts and with different semantics. The diffusion of the concept of governance accelerated with an emerging representation of society as an organized agglomerate of highly complex and functionally differentiated systems.

In the 1990s, the term became widely used within the context of international development aid. Worried about the poor results of their development agenda, the World Bank acknowledged that they were facing a “governance” problem, mainly caused by weak commitments and poor management (World Bank, 1989). The debates on governance broadened to characterize the “good governance” concept, which was seen as a transparent decisional process with sustainable goals for institutional development; an ethical, moral behavior, and lawful approach; strong participation from stakeholders; and was situated within a broadened perspective (World Bank, 1994).

In a corporate context, Cadbury’s (1992) seminal work defined governance as “the system by which organizations are directed and controlled,” and provided the foundations for the OECD’s “Principles of Corporate Governance” (OECD, 1999). For the OECD (2015), corporate governance is not an “end” (or a “goal”) in itself but rather a way to create market confidence and business integrity.

Most governance definitions somehow refer to decision-making and to the capacity to design, formulate and implement policies to achieve long-term strategic objectives. Yet, in many cases, decisions are taken based on personal experience, bounded rationality, and the inability to assess potential future outcomes. Decisions are actions taken by applying inference rules from our mental models to particular conditions perceived in the real world. The difference between good and poor decisions lies in a grey area between the process of information gathering and action implementation: in other words, it is based on how a small relevant fraction of all the available information is selected and effectively processed (Forrester, 1992) in setting up actions that aim to achieve a specific objective.

Unfortunately, when taking decisions, the human mind is limited by the availability of the very same information, its cognitive limitations, and the available time to process information and make a decision; hence, it cannot achieve the ideal of “objective rationality” (make the most optimal decision possible, given the information available) and is destined to have a lower level of the intended rationality (Simon, 1955).

Several techniques, technologies, practices, and methodologies are already being used and evaluated in complex and data-intensive scenarios that comprehend transmission, capture, storage, curation, analysis, visualization, and interpretation to improve decision-making processes (LaValle et al., 2014). Recently, a plethora of new technologies emerged in an attempt to deal with the astonishing amount of data that our society produces and consumes, and big data has started to attract a lot of attention over the last decade (H. Chen et al., 2012; P. Chen & Zhang, 2014).

Due to our modern digital life, where so much of our economic, social, and political lives takes place digitally and leaves digital trails everywhere, handling a vast amount of information in a timely manner can be crucial to transforming the mutual understanding and relationships between governments and citizens (Clarke & Margetts, 2014). However, the increasing information availability also creates a dilemma for decision makers, as Simon describes:

in an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it. (Simon, 1971, p. 41)

This constitutes a fundamental aspect in the actual transition of society to the “Digital Era Governance” (DEG), where digital technologies are currently being placed at the very core of bureaucracy (Dunleavy, 2005), almost in some kind of worship. Willis (2005) argued that good information and records management directly or indirectly underpins many of the vital aspects of corporate governance. Indeed, several mainstream research and application avenues place artificial intelligence, machine learning and data analytics at the core of the current and future capabilities of society to make sense of its complexity through deconstructing the complexity of the data it produces (H. Chen et al., 2012; P. Chen and Zhang, 2014; Vassakis et al., 2018). However, we know that data are just a symptom of a system behavior and not a direct proxy to anticipate future evolutions, especially for those mostly counterintuitive situations (Elragal and Klischewski, 2017; Simsek et al., 2019).

This is why some authors highlighted the potential benefits of combining seemingly opposed approaches, but instead complementary aspects such as governance, decision making, and analytics (Sterman et al., 2015). By combining these approaches, it is possible to leverage their strengths to overcome some previously identified weaknesses and the limitations of using them in isolation, thus improving decision-making processes based on better knowledge acquisition and representations to design “good governance” models, thus ultimately allowing better outcomes to be achieved.

There are many issues regarding the combination of knowledge creation, acquisition and processing with modeling and simulation, especially when aiming to achieve effective decision making and good governance.

Below are some examples of such issues that we plan to address in this Special Issue:

• Poorly posed problems tend to make people generate solutions that achieve poor level of knowledge (cognitive biases).
• Decisions need to be informed; therefore, they are based on knowledge.
• Poor knowledge biases following decision making.
• Effectively managing a large amount of data during decision-making processes is complex and not a simple process.
• Decisions are based on the information and knowledge available at the time of selection, which is not necessarily the best, most complete, or most accurate. Experimentation can improve the quality of decisions made from partial data.

Knowledge creation is an intrinsically circular process through which a set of valuable information is selected from information that a person has experienced or believes is not useful.

• Logically speaking, knowledge creation is a trial and error process, where experimentation provides the basis for selecting relevant information (on facts, processes, and relationships) that will allow for further experimentation with a decreasing number of errors.
• Several fields still appear to be explored in isolation. Building bridges to join them is necessary to evaluate if hypothesized benefits are feasible and how they could be arranged together.

The main objective of this Special Issue is to stimulate discussions around future pathways for developing new ideas around improved governance approaches that somehow intersect with the concepts described thus far. We seek to explore a new holistic, integrated, and systemic decision-making approach that we could bring together under the smart model-based governance (SMbG) umbrella (Armenia et al., 2017).

We invite research papers that seek to combine artificial intelligence and data analytics approaches with methodologies capable of describing (inter-) organizational functions, services, structures, processes, etc. These submissions should also consider modeling and simulation methodologies (even used in a hybridized/integrated fashion) and technologies capable of addressing strategic/vision/policy aspects in complex organizations (private or public) and environments, with an analysis of their relationships with governance and decision-making theories.

Empirical (experiments, surveys, case studies, etc.) and theoretical (e.g., conceptual, literature reviews, etc.) research papers are welcome. Submitted papers will be evaluated through a  peer-review process following the journal’s standard procedure.

References

Armenia, S., Franco, E. F., Mecella, M., & Onori, R. (2017). Smart Model-based Governance: from Big-Data to future Policy Making. BSLab - SYDIC International Workshop, 44–53.

Cadbury, A. (1992). The financial aspects of corporate governance. http://www.ecgi.org/codes/documents/cadbury.pdf

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165–1188.

Chen, P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347. https://doi.org/10.1016/j.ins.2014.01.015

Clarke, A., & Margetts, H. (2014). Governments and Citizens Getting to Know Each Other? Open, Closed, and Big Data in Public Management Reform. Policy & Internet, 6(4), 393–417. https://doi.org/10.1002/1944-2866.POI377

Dunleavy, P. (2005). New Public Management Is Dead--Long Live Digital-Era Governance. Journal of Public Administration Research and Theory, 16(3), 467–494. https://doi.org/10.1093/jopart/mui057

Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4(1), 19. https://doi.org/10.1186/s40537-017-0079-2

Forrester, J. W. (1992). Policies, decisions and information sources for modeling. European Journal of Operational Research, 59(1), 42–63. https://doi.org/10.1016/0377-2217(92)90006-U

Hufty, M. (2011). Governance: Exploring Four Approaches and Their Relevance to Research. In U. Wiesmann & H. Hurni (Eds.), Research for Sustainable Development: Foundations, Experiences, and Perspectives (pp. 165–183). NCCR North-South, Centre for Development and Environment (CDE) and Instiute of Geography, University of Bern.

LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2014). Analytics: The new path to value. MIT Sloan Management Review, 52(1), 1–25.

OECD. (1999). OECD Principles of Corporate Governance. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=C/MIN(99)6&docLanguage=En

OECD. (2015). G20/OECD Principles of Corporate Governance 2015. OECD Publishing. https://doi.org/10.1787/9789264236882-en

Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118.

Simon, H. A. (1971). Designing organizations for an information rich world. In M. Greenberger (Ed.), Computers, communications, and the public interest (pp. 37–72). The Johns Hopkins Press.

Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S., & Shaw, J. D. (2019). New Ways of Seeing Big Data. Academy of Management Journal, 62(4), 971–978. https://doi.org/10.5465/amj.2019.4004

Sterman, J., Oliva, R., Linderman, K., & Bendoly, E. (2015). System dynamics perspectives and modeling opportunities for research in operations management. Journal of Operations Management, 39–40(1), 1–5. https://doi.org/10.1016/j.jom.2015.07.001

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big Data Analytics: Applications, Prospects and Challenges (pp. 3–20). https://doi.org/10.1007/978-3-319-67925-9_1

Willis, A. (2005). Corporate governance and management of information and records. Records Management Journal, 15(2), 86–97. https://doi.org/10.1108/09565690510614238

World Bank. (1989). From crisis to sustainable growth - sub Saharan Africa : a long-term perspective study. http://documents.worldbank.org/curated/en/498241468742846138/From-crisis-to-sustainable-growth-sub-Saharan-Africa-a-long-term-perspective-study

World Bank. (1994). Governance - the World Bank’s experience. http://documents.worldbank.org/curated/pt/711471468765285964/pdf/multi0page.pdf

Dr. Stefano Armenia
Dr. Eliot H. Rich
Dr. Eduardo Ferreira Franco
Guest Editors

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