4.3 Developing Principles to Guide STSs Analysis and Design
4.3.1 Complexity Perspective on Dynamic Socio-Technical Environments
Social sciences appear to seek improved scientific legitimacy by copying the century-old linear deterministic modelling of classical physics—with economics in the lead (Henrickson
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Set of concepts that attempts to explain complex phenomenon not explainable by traditional (mechanistic) theories. It integrates ideas derived from chaos theory, cognitive psychology, computer science, evolutionary biology, general systems theory, fuzzy logic, information theory, and other related fields to deal with natural and artificial systems as they are, and not by simplifying them (breaking them down into their constituent parts). It recognises that complex behaviour emerges from a few simple rules, and that all complex systems are networks of many interdependent parts, which interact according to those rules.
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and McKelvey, 2002); at the same time, natural sciences previously strongly rooted in linear determinism are trending toward nonlinear computational formalisms. The postmodernist perspective takes note of the heterogeneous agent ontology of social phenomena, calling for abandoning classical normal science (described by Thomas Kuhn (Hoyningen-Huene, 1993)) epistemology and its assumptions of homogeneous agent behaviour, linear determinism, and equilibrium. Nevertheless, postmodernists seem unaware of the ‘new’ normal science alternatives being unravelled by complexity scientists. These scientists assume, then model, autonomous heterogeneous agent behaviour, and from these models study how supra-agent structures are created. Scrapping the equilibrium and homogeneity assumptions and emphasising instead the role of heterogeneous agents in social order-creation processes is what brings the ontological view of complexity scientists in line with the ontological views of postmodernists. Example, as in spontaneous order creation of the ‘melting’ zone (Kauffman, 1993) which begins when three elements are present:1) Heterogeneous agents 2) Connections among them
3) Motives to connect – such as mating, improved fitness, performance, learning, etc.
Therefore, from the complexity science point of view, the network structure of the organisation and environment are constructed by autonomous agents, interacting with each other internally and externally, from which evolution in the agent parameters will emerge: this could be reflected in their behaviour and in their impact on environment. These agents interact with a level of freedom for self-organisation and situational confirmation. The agent concept is presented in complexity science as a collection of properties, strategies and capabilities to interact with artefacts and other agents within the context.
The concept of emergence in complexity science has been adapted to many cross-domains in organisational behaviour, leadership, market strategy, risk assessment and mitigation (Ellis, 2004; Henrickson and McKelvey, 2002; McKelvey, 2010a). The concept of emergence was applied for engineering the software part of a system, leading to an agent-oriented software system. Moreover, it can be adapted on a methodological level. Agent-oriented techniques can make a substantial contribution to the implementation of information systems by providing additional functionality and better user interfaces. In software engineering activity, the advantages of the agent concept over other concepts like that of object is not obvious and may depend on the nature of the system to be developed. Conversely, for the requirement’s engineering activity, the concept of the agent seems necessary because of the need to model
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the environment of the system and because of the natural decomposition of this environment in terms of agents. The concept of object (Meyer, 1988), though resembling that of agent in some respects, lacks some properties that are needed when defining requirements. In classical object-oriented modelling, communication among objects is often simplistic with regards to that occurring among real world entities and is usually limited to message passing or synchronisation among objects. Communication in agent languages is usually far more developed (see e.g. Finin et al., 1997) and allows for communication actions with a higher semantic content and agents with greater autonomy having the capability to decide when they communicate or not.Furthermore, McKelvey’s (2004b) Distributed Intelligence (DI) is an important concept referring to the intelligence of all agents as an evolution in intelligence. The fundamental point of DI is that the intelligence of the collective of agents is not equal to the sum of their individual intelligence; it is usually greater than the sum.
The adaptive tensions (McKelvey, 2010b) created by discrepancies between the requirements in the environment and the actual resources of the organisation may lead to an organisational response in the form of an emergent overall strategic direction that cascades down the organisation in the form of team goals or assigned tasks. For example, when organisation implement new technology, there is always level of tension between this technology and the new environment where it has been implemented, adaptive tension play a role of force to adapt such as technology, this force could be managerial from high-level management to implement such as technology in the environment. To address the complexity of their task environment, agents and their interactions reconfigure themselves by continuously creating new and unpredictable forms of emerging order (Holland, 1998). I argue that the discrepancies not only happen to the resource: the environmental dynamics influence the enterprise system requirements and indirectly influence the resource. As the environment is unpredictably dynamic and the requirements may not feature accuracy and clarity, thus the need arises to make the resources continually evolving for self-organisation and conformation to the new requirements. Figure 12 is a conceptualisation of the complex dynamic interaction in complexity theory.
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Knowledge Thinking Individual Group Agent * * * * * * Evolution * *Decision Emergent Behaviour * *
* *
Belonging to the system rules Outside the system rules
Adaptive Tension
Change in the Environment * * * * * * * *
FIGURE 12: COMPLEXITY AND DYNAMIC CONCEPTUAL MODEL
- Interaction among agents causes group thinking: this will speed up learning and cause distributed intelligence, which gives rise to knowledge emergence.
- Agent evolution causes change in the way an agent interacts with other agents: "interaction protocols". This evolution will increase heterogeneity among the agents and will affect the motivation of connection.
- The emergent behaviour of the organisation in response to the pressure of adaptive tension can come in the form of new organisational requirements: these may require new or adapted resources.
- The emergent behaviour of individuals can arise from self-organisation if it follows the rules and conforms to the organisational goals.
- The emergent behaviour could represent a risk if the agent acts outside the rules or against the organisational goals.
- However, the emergent behaviour in return will result in a change that also influences the environment.
Table 10 gives a semantic mapping between complexity theory constructs and socio-technical constructs based on the methodology of framework for theory development (Kuechler and Vaishnavi, 2012) DREPT steps:
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TABLE 10: MAPPING COMPLEXITY CONSTRUCTS TO SOCIO-TECHNICAL CONSTRUCTSComplexity theory construct/proposition
Socio-technical construct/proposition
Semantics
Heterogeneous Agents Agent/actor Agents/actors in socio-technical systems have different types, some interact internally and some externally
Agent Motivation Motivation/Goal In socio-technical systems, motivation and goals could be for an individual agent or for a group of agent
Self-Organising Independency and freedom (bottom-up)
Agents have the ability to behave freely either to support or harm the system (need control in terms of human agents, and need intelligence as software agents)
Control Mechanism Control (Top-down) Directives required to govern the socio- technical system design and operation, also to direct the agents’ behaviour
Adaptive tension Event based directives and enforcement
The adaptive tension is an event based motivation objective, it is work as an energising device to help in self-organising and can be a negative or positive directive: it can be adapted to socio-technical systems as event based directives
Distributed Intelligence
Collaborative knowledge
Collaboration among agents produces an evolution in knowledge from explicit to tacit which needs to be codified again
Evolving Evolving Evolving is a socio-technical system
characteristic, and can involve evolving in knowledge, evolving in structure and evolving in the interactions. Evolving can be manual by a human agent, or automatic by a software agent.
The previously adopted principles will help to form new principles for understanding, analysing and designing socio-technical systems. These principles should be considered when developing the prospective modelling framework.