DIGITAL ECOSYSTEMS, ONTOLOGY, ENTROPY, by Paola Di Maio
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DIGITAL ECOSYSTEMS, ONTOLOGY, ENTROPY, by Paola Di Maio DIGITAL ECOSYSTEMS, ONTOLOGY, ENTROPY, by Paola Di Maio Presentation Transcript

  • DIGITAL ECOSYSTEMS, COLLECTIVE INTELLIGENCE, ONTOLOGY AND THE SECOND LAW OF THERMODYNAMICS Paola Di Maio, MFU . AC . TH, Chiang Rai, Thailand e - mail : paola . dimaio at gmail dot com
  • SUMMARY
    • Digital Ecosystems are interconnected environments which enable the proliferation and harnessing of Collective Intelli-gence.
    • Ontologies built in support of information systems that constitute the fabric of digital ecosystems need to take into ac-count dynamic factors which contribute to ‘ongoing change in underlying reality.
    • Approach : still speculative, extremely interdisciplinary (we mix theories from various disciplines in the spirit of domain convergence)
    • Contribution : After literature review, ‘entropy’, as defined in the second law of thermodynamics, is considered as a possible factor of change that needs to be modelled in ontology engineering
  • INTRODUCTION
    • INFORMATION SYSTEMS: pervasive and adaptive, ca-pable of supporting and storing as much data as it is generated by the day by individuals and organisations and to respond to changes in the digital environments in which they reside.
    • CHALLENGES: challenges, such as defining optimal knowledge rep-resentation for complex and dynamic models of reality, configur-ing automated discrete reasoning capabilities.
    • ONTOLOGIES: important artefacts necessary to support the design and maintenance of evolving 'digital ecosystems'.
    • CHANGE AND TRANSFORMATION key factors that should be reflected in knowledge representation and models of reality,
    • THEREFORE future developments in ontol-ogy engineering methodologies include mechanisms to model 'change' and 'transformation. Our future research aims to evaluate the relevance of ‘entropy’ based methods as a possible way for-ward in this direction.'
  • DIGITAL ECOSYSTEMS
    • Ecosystems , attributed first to A. G. Tansley (1935) de-rived from the words ecology, from the Greek oikos "habitat", and system, a 'unit' or whole made up of different components which generally interact to establish and maintain some level of 'equilibrium' .(system equilibrium)
    • 'Digital ecosystem ' refer to extended, interconnected environments where information is exchanged digitally by its components: the internet is a large, dynamic and open digital eco-system where individual users and their software 'agents' are the smallest components, that contribute to the creation and consumption of digital information.
  • COMPLEXITY
    • If natural ecosystems are complex and their internal dynamics largely still unexplained in terms of individual behaviours, Digital Ecosystems (DE) are infinitely more complex and challenging to observe and understand, in that they emerge from the intersection of the social and information technology (cybernetics) dimensions, already highly complex when analysed separately, and exponentially so when combined.
    • COMPLEXITY :
    • QUANTITY OF OBJECTS/INDIVIDUALS OBSERVED
    • PATTERNS OF BEHAVIOUR REGULAR/NOT REGULAR
    • GRANULARITY
    • INTERACTION
    • INTERDEPENDENCE
    • EXTERNAL EVENTS
    • CHANGE
  • COEVOLUTION
    • Co-evolution of diverse elements interacting within a social ecosystem is defined as a series of recip-rocal steps during which two or more ecologically interacting spe-cies respond to one another evolutionarily. There are different models of co-evolution in social sciences also referred to as 'inter-living', co-adaptation, and to some extent, co-opetition.
  • SELF ORGANIZATION
    • the ability of individual components of an eco-system to develop internal and external dynamics that provide self synchronization within the system
    • See also: autopoiesis , autocatalysis
    • Research investigating how the DNA is a carrier of social intelligence has been done in bacterial colonies demonstrating, albeit under a microscope, how social intelligence exists thanks to communication and information exchange at cellular level.
  • COLLECTIVE INTELLIGENCE
    • Collective Intelligence:
    • reasoning capability (humans plus machines),
    • coupled with the sum of existing explicit knowledge that can be repre-sented and accessed by such reasoning engines.
    • Several initiatives have been started at academic, social and entrepreneurial level to study and capture the potential of collective intelligence. While knowledge bases are being developed online in real time (wikipe-dia, dbpedia), software applications and environments that support collective reasoning are still largely at research stage.
  • BENEFITS OF COLLECTIVE INTELLIGENCE
    • • Catalyze networked effects
    • • Create high transaction rates
    • • Enable rapid adaptation to dynamic conditions
    • • Execute distributed or concentrated operations
    • • Self-organize decision making – defined by simple rule sets
    • • Generate “organic intelligence” and leverage Global intelli-gence
  • BUSINESS INTELLIGENCE AND TRASFORMATION
    • Detect and predict , patterns of change, to align internal organisational resources and processes that must constantly be readjusted due to some change occurring in another part of the system, that inevitably reflects on all its components.
    • The ability of an organisation to transform, be flexible and adapt, delivers competitive advantage .
    • Business transformation is an executive management technique to align the technology initiatives of a company more closely with its business strategy and vision. The degree to which a company can imple-ment new initiatives to support changes in business strategy is known as business agility . Thanks to transformation, operations can adapt to rapid strategic changes . When conditions change, goals and plans need to be adapted, and the set of organizational assets — data, processes, people — as well as everything that follows “change” need to be modelled accordingly.
  • Transformation can happen at three levels
    • Business model transformation is a key strategic practice of enabling and supporting — with suitable methods and techniques — adaptation in an organizational environment through changes.
    • Data transformation is the process of redefining data based on some predefined rules that generally embed some kind of en-terprise and business logic. The data values are redefined based on a specific formula or technique.
    • Process transformation is concerned with supporting the ad-aptation, evolution, and optimizations of organizational processes, which are actually “ongoing” — think, for example, of “process improvement.”
  • COLLABORATIVE ONTOLOGIES
    • Ontologies are conceptual and semantic frameworks repre-senting models of the world, as well as explicit and complete knowledge representation of a model of reality, expressed using different formalisms and artefacts.
  • TRANSFORMATIN CHALLENGES ARE CONCEPTUAL
    • Researchers who have studied business process modelling in detail have identified, however, that most of the problems with transformation, even the most practical ones, are of a conceptual nature and so they could hardly be solved with technical innova-tions, rather they need to be addressed at model and representation level
  • TRANSFORMATION = ENERGY EXCHANGE
    • According to the law of conservation of mass, (aka first law of thermodynamics), 'nothing is lost, and nothing is created, everything is transformed'. To be able to leverage the principles of transformation, and learn how to apply them in the context of so-cial and information systems, the social sciences look at physics, aiming to capture insights into the natural laws that may drive change. Information plays a key role in the ability of an organism to adapt, respond and survive to environmental 'changes'
  • ENTROPY
    • Another important assertion of the key nature of transformation is found in the second law of thermodynamics, which is essentially a general principle about the dynamic of 'energy dispersion' (http://www.secondlaw.com) also known as the Law of Increased Entropy, which is a measure of unusable energy.
    • Entropy has been expressed and studied in differ-ent forms by different scientists, namely Canot, Clausius, Boltz-mann, Gibbs , Shannon. Each approach proposes a different interpretation relevant to its context: some define entropy in terms of 'energy exchange', others in terms of 'ability to do work', others in terms of 'measure of organization of a system'', and so on, until more recent interpretations - entropy as semantic distance between two words, or as imprecision in the translation of a term in two different languages.
  • ENTROPY AND OPEN SYSTEMS
    • As usable energy decreases and unusable energy increases, "entropy" increases. Entropy is also defined as a measure of randomness or chaos, as us-able energy is irretrievably lost, disorganization, randomness and chaos increase.
    • However it was not until Prigogine's times, (he received the Nobel Prize for chemistry in 1977) who studied en-tropy applied to chemistry to biology, and contributed to the emer-gence of chaos theory and complexity theory , that entropy has been said to apply also to open systems, and not just to closed sys-tems as previously believed.
  • ENTROPY AS RANDOM VARIABLE
    • In the context of signal processing and communication theory Shannon [8] famously related entropy to information and defined it as a "measure of the uncertainty associated with a random variable".
    • Although some argued that the concept of 'entropy' applied to physical science as stated in the second law is not di-rectly comparable to the notion of entropy in the communication theory context, the idea of information as 'available energy' is be-coming increasingly acceptable in the information age.
  • 'LAW OF MAXIMUM ENTROPY PRODUCTION'(MEP), AKA 'LAW OF SPONTANEOUS ORDER'
    • The 'Law of maximum entropy production'(MEP), also known as the 'law of spontaneous order' takes the second law a bit further. It says that 'entropy production is maximized at the fastest rate given the constraints' meaning that systems are inherently capable of selecting the most efficient route (which happens to coincide with the most organized system state) to compensate for disequi-librium and reach stability (minimum entropy state)
  • ENTROPY AND ONTOLOGY
    • Today, knowledge management and decision support systems in enterprises are becoming increasingly 'ontology based', that is, they rely on the existence of an accurate view of the world to model the relevant supporting systems.
    • Specialised information and decision support systems depend on correspondingly special-ised ontologies: medicine, aerospace, civil engineering.
    • Pefferly, Jaeger and Lo in a recent study make the case for taking into account 'entropy' as well as other variables, when building ontol-ogy based knowledge management systems They say "when de-scribing information Based systems, statistical measures are a ne-cessity; yet very few ontology based standards mention quantifiable measures such as entropy, data encapsulation, complexity, efficiency, evolution, or redundancy"
  • David Wolpert, reasearcher with NASA 'Probability Collectives',
    • Principles underlying information theory could be the binding glue among many disciplines "…Information theory has been ex-tensively applied to many other disciplines, for example, in the guise of the maximum entropy principle, it has found great appli-cability in data processing and analysis. More generally, it is now recognized that there are a host of statistical inference techniques related to information theory which have proven very powerful in many different fields” Wolpert says that in the future, information theory's greatest new contributions will be to *relate* disciplines and contribute to scientific convergence.
  • CONCLUSION AND FUTURE
    • Human understanding of how the second law of thermodynamics apply to digital ecosystems is still limited,
    • Having researched and demonstrated through literature review the rele-vance of the second law of thermodynamics to Business Intelli-gence,
    • We put forward the hypothesis that 'entropy' as a measure of change and transformation is a factor in models of reality, and as such, it should be modelled accordingly.
    • IF
    • ontology represents reality (or a bounded subset thereof),
    • and reality changes/transforms constantly,
    • and entropy provides a measure to calculate/predict such changes
    • THEN
    • ontology engineering should use entropy measurement (as well as possibly other methods)
  • QUESTIONS?
    • Paola Di Maio