QSIM is a qualitative reasoning algorithm developed by Kuipers [Dalle Molle et al., 1988; Kuipers, 1986]. The purpose of QSIM is to explain system behavior from the physical descriptions, even if the description is incomplete. It starts from a set of constraints and produces all the possible future states that are consistent with the description. The qual-
itative states are represented as landmark values and direction of change. Then possible behaviors can be visualized as graphs. QSIM models a system behavior as a sequence of states constituting a path from the initial state to the final state [Dalle Molle et al., 1988]. Moreover, QSIM is able to construct qualitative phase space to depict dynamical behaviors [Lee and Kuipers, 1993].
Our work is similar to QSIM in several aspects. Both works focus on system dynamics. Phase space plays an important role in explaining system behaviors. A path of states is used to delineate a behavior. QSIM aims to qualitatively reason a system’s behavior given only partial information. The essence of QSIM is to construct Qualitative Differential Equations (QDEs) and solve them to get system’s qualitative behavior. However, our study is not interested in how to obtain the dynamics. Instead, we focus on explaining emergent phenomena via teleodynamics. The examples presented in Section 5.5 have dynamics as differential equations or casual relationships. System’s teleodynamics can also be given as QDEs.
If we construct QDEs for the level control tank system (Section 5.5) and plot the quali- tative behaviors of both the “starting low” and the “starting high” scenarios (details about QSIM model construction can be found in Appendix D), as shown in Figure 5.9(a) and Figure 5.9(b), we find the two scenarios reach the same final state, i.e., the set point, as expected.
(a) (b)
Figure 5.9: Qualitative behavior of the linear level control tank system
settles down at the set point. It confirms the behavior we obtained in Section 5.5.
Figure 5.10: Qualitative phase portrait
5.7
Chapter Conclusion
In this chapter, we illustrate emergent behaviors of sociotechnical systems by studying teleodynamics. A formal representation is developed to model the teleodynamics of so- ciotechnical systems at any level. It describes a sociotechnical system in terms of classes and sets, and system behaviors in terms of functions and states. Examples show systems’ control behaviors in the phase space as “the whole more than the sum of parts.” Phase space trajectory illustrates the transition of system states, thus, delineates the evolution of a system. Every point in the phase space represents a micro-state. The trajectory cannot be induced from an individual micro-state. As a result, we answer the question “how simple individual components of a system interact to result in a system behavior that cannot be explained by any components alone.”
Classical dynamics studies the evolution of a system. It does not care about the part-whole relationship. As a result, classical dynamics is able to explain what emergent behavior is, however, unable to answer how the behavior emerges. In contrast, teleodynamics con- cerns both teleology and dynamics. It demonstrates system’s part-whole relationship using teleology and complex behaviors using dynamics, hence, uncovers the mystery of emergence. Chemical engineers study the complex dynamics of chemical processes using control theory, but rarely think about its complexity science implications. We demonstrate that a control behavior is in fact an emergent behavior, hence, bridge the knowledge gaps between chemical engineering and complexity science.
Chapter 6
Conclusion Remarks
The road ahead will be long, I shall search.
Qu Yuan
To ensure the safe operation and production of complex sociotechnical systems, we need
to model and analyze systemic risk. Traditional emphasis of chemical engineering risk
analysis is on equipment and processes. However, systemic risk management studies not only equipment and processes but also human activities. This means classical quantitative approaches are no longer satisfactory. It is critical to model different kinds of knowledge of a sociotechnical system.
In this thesis, we develop a new knowledge modeling paradigm that goes beyond tradi- tional risk modeling in chemical plants. Specifically, we develop the TeCSMART framework to model system knowledge, We use SDG to model cause-and-effect knowledge and ontol- ogy to model heuristic knowledge. We study system’s teleodynamics to answer the question “how simple individual components interact to result in a system behavior that cannot be explained by the behavior of just the individual components alone.”
6.1
The Roles of Teleology, Feedback, and Emergence
Our study emphasizes the roles of teleology, feedback, and emergence in modeling systemic risk. A teleological framework is established to model sociotechnical system as a whole by
integrating both social elements and technical elements via the goal-driven activities. The framework models system knowledge to systematically analyze risk associated with differ- ent levels of sociotechnical systems. Teleology also helps develop an ontological document knowledge model, which supports public health decision making during EID emergencies.
Feedback is widely observed in complex dynamical systems. A positive feedback loop usually indicates a run-away situation. By modeling system’s cause-and-effect knowledge, we can identify positive feedback loops in a complex financial network. These feedback loops explain the hidden instability of a sociotechnical system.
Moreover, emergent behavior is a result of the aggregate effect of sociotechnical system’s dynamic, goal-driven activities in the multi-layered hierarchy. The underlying part-whole relationship can be illustrated in the phase space. Teleodynamics integrates teleology with system dynamics, therefore, explains how systemic risk emerges in complex sociotechnical systems.