In recent decades, many EID outbreaks and epidemics have resulted in considerable human disability and mortality in part due to ineffective coordination or slow response at the start of the outbreak. Responding to EID outbreaks is intrinsically challenging due to the un- certainties associated EID, specifically level of risk and potential the impact of its spread in a population. During an outbreak, evidence-based public health policies developed by public health authorities, legislators, and other government officials facilitate the implemen- tation of a strong public health response. However, there are structural and political forces that prevent decision makers from making evidence-based policies in response to outbreaks.
RESPONSE
Therefore, it is necessary to have in place a mechanism to easily identify evidence in order to evaluate the consequences of public health or policy actions recommended to address these public health emergencies. An ontology framework for public health outbreak response will cut the time spent aggregating expert opinions during the initial stages of an outbreak. It would also assist public health administrators and government officials on next steps based on individual- and systems-level factors associated with the outbreak.
This approach manages document knowledge for the regulatory and government layers of a public health system. It introduces a systematic way of storing, retrieving, and using public health knowledge. Accuracy and comprehensiveness of decision making can be im- proved as more knowledge is stored in the ontology. It is a potentially effective methodology for EIDs preparedness and response.
Chapter 5
Modeling Emergent Phenomena of
Dynamical Sociotechnical Systems
Nothing endures but change.
Heraclitus
In previous chapters, we discussed how to model system knowledge, cause-and-effect knowledge, and heuristic knowledge for a sociotechnical system. Systemic risk management
requires the understanding of system’s emergent behaviors. In this chapter, we model
system’s teleodynamics, i.e., the goal-driven dynamics, to study emergent behaviors to answer the question, “how do simple individual components of a system interact to result in a system behavior that cannot be explained by the components alone? ” This has been a long standing open question, especially from a control-theoretic perspective.
We investigate simple systems to understand how interactions of parts lead to unex- pected behavior of the whole. People may wonder how simple systems could help explain emergence in complex systems. However, science and engineering are full of examples of simple models that give useful insights about complex phenomena even though they may miss some of the details.
5.1
Emergent Behaviors in Dynamical Sociotechnical Sys-
tems
A chemical plant is a multi-layer hierarchical structure where information or materials flow within each layer or through different layers via goal-driven processes. This hierarchical structure can be modeled as a seven-layer input-output framework, depicted in Figure 2.1. At each layer, elements achieve their goals via their functions. For example, a level controller of a tank system has the goal to maintain the level at its set-point. The controller achieves this goal by tuning the electronic signal of valve pressure. When elements (e.g., controller) have realized their goals, the system (e.g., level control tank system) achieves its desired status. This is a goal-driven process. A chemical plant is a hierarchy of such networked processes. One level is an aggregation of processes of the adjacent level below it. When low- level processes execute their goals, the aggregate effect makes the system at the high-level evolve a new state. Ideally, this new state is the goal of the high-level system. However, as the system becomes more complex, it might evolve towards a state that is not a desirable one. For example, BP Texas City refinery and Deepwater Horizon oil rig are at the plant level while BP as a company is at the company level. The flawed activities at the BP plants can lead to unexpected state of BP, i.e., a vast monetary loss and reputation crisis. The whole event is a systemic failure. The goal-driven activities in multi-layered hierarchy lead to emergent behaviors, some of which are undesirable.
To ensure safe operations over the life cycles of chemical plants, we need to design, analyze, and model their behaviors, and manage the potential for increasing systemic in- stability and fragility [Centeno et al., 2015; Fouque and Langsam, 2013]. This requires the representation of system behavior focusing on the mechanisms generating behavior in the actual, dynamic work context [Rasmussen, 1997]. Along these lines, some researchers try to understand the system’s self-organizing behavior [Bialek et al., 2012; Feistel, 2016; Hemelrijk and Hildenbrandt, 2011; Polani, 2013; Reynolds, 1987]. Others study the com- plex dynamics of engineered systems using chaos theory [Hirsch et al., 2012] and control theory [Leveson and Stephanopoulos, 2014; Ogunnaike and Ray, 1994; Seborg et al., 2011]. These studies focus on explaining what is emergent behavior. However, the question how
simple individual components interact to result in a system behavior that cannot be ex- plained by the behavior of individual components alone has not been explicitly answered in a control-theoretic setting.
In this chapter, we try to answer this well-known question in complexity science from a control-theoretic perspective. We explain how goal-driven behaviors propagate and ag- gregate in a hierarchical sociotechnical system. This chapter unfolds as follows. First, we review both the philosophical and the scientific definitions of emergence. Next, we argue that the study of emergence needs to investigate goal-driven dynamics. We introduce a for- mal representation to illustrate emergent behaviors of different systems. We also compare our approach with Qualitative Simulation (QSIM).