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Self-regulation in complex social systems: biological metaphors

Self-regulation in complex social systems: biological metaphors. Self-regulation in complex social systems: biological metaphors. Why we need self-regulated social systems Lessons from natural systems Self-regulation: Research approaches Implications for organizations

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Self-regulation in complex social systems: biological metaphors

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  1. Self-regulation in complex social systems:biological metaphors

  2. Self-regulation in complex social systems: biological metaphors • Why we need self-regulated social systems • Lessons from natural systems • Self-regulation: Research approaches • Implications for organizations • Research projects: some seed questions

  3. Why we need to learn about self-regulation on complex social systems? Hypotheses: • Current society: Clearly non-sustainable: • Inadequate variety management and therefore to, • Inadequate structural arrangements in the relationships: • communities. vs. government and • industries .vs. governments • Wide agreement on understanding organisations as complex adaptive systems, but: • not enough understanding of internal learning and adaptation mechanisms

  4. What can we learn from natural self-organized systems?

  5. Organisational Cybernetics Complexity management theory using variety engineering Focus on structural arrangements for viability. VSM: Viable System Model: for structural diagnosis and design Cybersyn: Originating from Chile : the tools and procedures needed for implementation. Complexity theory Based on the mathematics of non-linear dynamics Organisations modelled as Complex Adaptive Systems. Patterns of behavior emerge in irregular but similar forms through a process of self- organization Self-organization is governed by a small number of simple order-generating rules. Self - regulation: Organizational Cybernetics .vs. Complexity Theories

  6. Organisational Cybernetics “The science of effective organisation” (Beer, 1966) Complexity: What an observer distinguishes from a particular situation in a specific domain of action. Variety: The number of different states that the observer distinguishes Complexity and Systems Sciences Complexity as science Study of non linear systems Complexity as a method of thought Overcomes limitations on traditional epistemology: learning to think relationally Complexity as worldview Articulation of new understanding on both the world and knowledge, takes advantage of holistic, systemic thinking Different ideas of complexity?

  7. Organisational Cybernetics (Viable Systems) Identity - boundary Complexity - variety Modelling - learning- closure Equifinality - viability Structure - Process Recursive Organisation Self-regulation - Adaptation Homeostasis - Autopoiesis Complexity Sciences (Complex Adaptive Systems) Dynamic non-linear systems Chaos .vs. order No causal relationships Unpredictable order Self-organization Order generating rules Basic concepts on self-regulation

  8. Re-distribution of responsibilities: Based on variety management True democracy: Devolved control to operational levels Meta-systemic management: not ‘cognitive autocracy’ Performance management: based on self-regulating units Need for greater democracy and power equalization Continuous transformation, based on self-organization. ‘Order generating rules’: to overcome the limitations of rational, linear, top-down, strategy-driven approaches to change. Implications for organisations

  9. Ineffective government efforts for town & rural regeneration and need for innovative- effective structural arrangements. Difficulties to progress towards a ‘green’ organization (e.g. Hull University, Scarborough Campus) Lack of effective support for development of the regional organic sector (e.g. Yorkshire and Humberside region) Self-regulated business: beyond financial management? PhD project to design implementation networks more able to foster sustainable development: networking multiple local actors in policy implementation. PhD project: Understanding social .vs. biological individuals and networks; engraining sustainability into culture and structure. Regional Organic Network: Action research into business .vs. community .vs. government interactions to suggest policy and organisational developments. PhD project: Implications of VSM for financial management Seed problems/ research aims

  10. Defying the rules: How self-organization works in social systems1 • To understand processes of self-organisation in a complex societal context from the perspectives of physics, biology and social sciences • To illustrate the way basic rules for self-organisation present in other levels of organization apply to improve organizational design for regeneration programmes. • To develop an understanding of social organisations from the perspectives of holistic and complexity sciences. • EPSRC - Emergence and Complexity Network 2006-2010

  11. Self-organization: Mechanism for building patterns, processes and structures at a higher level through multiple interactions among the components at the lower level, where the components interact through local, often simple rules that do not explicitly code for the pattern ( Camazine & Deneubourg 1994) ‘Components’ (in human social systems): human beings, autonomous, self-conscious (have identity) and purposeful (have intentionality); even if they follow rules for social interaction, they can decide not to follow them anymore or even to change the rules. Not all rules are local or simple! Self organizationBiology .vs. social sciences

  12. Division of labour: Occurs when all constituent entities are co-adapted through divergent specialization so that there is a fitness or inclusive fitness gain as a consequence of such specialization. Several studies have demonstrated the impact of structure on performance. Selection pressures for increasing efficiency of such systems have favour division of labour. Social resilience: Ability of ant colonies to reassemble after a massive disruption so that workers re-establish their spatial positions ….It is based on self-assembly through self-organization. In most social organisations: individuals are multi-task agents. Structure varies from hierachical to flat –networked-: the first one more suitable in stable environments, the latter in complex, changing environments. Same phenomena. Division of labour is the most natural mechanism to handle the complexity of a particular task In hierarchical organisations the only response after a massive disruption may come mainly from the leaders. Division of labour .vs. performance

  13. Research questions Why certain structures were favoured by natural selection? How is a certain pattern or structure created? Is specialization through learning likely to be of crucial importance in the assembly of organisms into biological and human societies ‘of all kind’? and artificial? Which values/ societal regulators those societies shared? Research hypothesis Self-assembly involving self-organization: should have been favoured by natural selection; b.c. it’s more robust, simpler, more secure against failure and is likely to have the capacity for self-repair. Which historical/contemporary examples we can find of self-organising communities, industries and societies? How does specialization affects learning in self-organised social organisations? How does the structure affects performance? How does issues of reputation and cultural norms affect performance in self-organised collectivity? Can we model/design informal network interactions using VSM? Could we use these criterion to design community programs (e.g. for regeneration) promoting self-assembly and self organization? Research questions

  14. Open questions • Are there structural patterns leading to emergence of new organisational forms that repeat at different levels of complexity (e.g. biological/social) • Can we identify them, model them and use them to design complex social organisations? • How can we best use organisational cybernetics to explain these rules of interaction in self-organized (self-assembled structures)?

  15. Self-organization and social systems: lessons for regeneration programs • To study the current body of knowledge explaining self-organisation on both biological and social systems and find out differences and similarities in basic concepts and models. • To study success and failure in regeneration programs, leaded under traditional approaches and find out structural patterns shared by them that may be susceptible of re-design or social intervention. • To develop methodology and tools, inspired in generic rules of self-organisation that will support development and implementation of regeneration programmes, as self-regulated informal networks of interaction. • To test the validity/usefulness of the tools in an agreedreal life situation.

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