Stefan Pistorius 1
The evolution of knowledge
A unified naturalistic approach to evolutionary epistemology taking into account the impact of information technology and the
Internet
Pistorius, Stefan
private
Head of Software Department
Stefan Pistorius 2
Agenda
• Introduction
• An adaptive network model of ‘knowledge’ and ‘knowledge evolution’
• The topology of the global knowledge network and epistemic consequences
• The future of the global knowledge network?
Stefan Pistorius 3
Initial question
How can we understand the impact of information technology and theInternet on the evolution of human knowledge?
Can evolutionary epistemology give answers?
Stefan Pistorius 4
Two branches of evolutionary epistemology(acc. to Bradie & Harms)
• The Evolution of Epistemological Mechanisms (EEM)
– a straightforward extension of the biological theory of evolution
– focuses on the evolution of sensory systems and brains to make survival more likely
– natural selection responsible for the evolution of epistemological mechanisms
– exponents of EEM: Konrad Lorenz, Donald T. Campbell, Gerhard Vollmer
• The Evolutionary Epistemology of Theories (EET)
– accounts for the development of knowledge within knowledge communities
– focuses on the evolution of 'ideas', 'scientific theories' and culture in general
– (natural) selection responsible for the evolution of theories
– exponents of EET: Karl Popper, Donald T. Campbell, Philip Kitcher
• The challenge: To find a model in order to describe aspects of EEM and EET and the influence of computers and the Internet!
Stefan Pistorius 5
Agenda
• Introduction
• An adaptive network model of ‘knowledge’ and ‘knowledge evolution’
– Interactive Adaptive Turing Machines
– Individual ‘world views’
– Supra-individual ‘knowledge domains’ and
– ‘Knowledge Evolution’
• The topology of the global knowledge network and epistemic consequences
• The future of the global knowledge network?
Stefan Pistorius 6
Interactive Adaptive Turing Machines (IATM)
An interactice adaptive Turing machine (IATM) is a device that
• receives an unbounded sequence of messages (i.e. finite strings) from
other IATMs or 'sensorial data messages' from nature via its input ports,
• does 'computations’ based on the input and its memory content(!) and
produces an unbounded sequence of output messages via its output ports,
and
• has an unbounded persistent read/write memory to 'memorise' data /
factual knowledge (i.e. messages and message patterns) as well as its
algorithmic rules / transformational knowledge.
A set of interacting IATMs constitutes an adaptive knowledge network.
Theorem: For every finite set S of IATMs exists a single IATM M that
sequentially implements the same computation as S does.
A network of IATMs can still be seen as a unity!
Stefan Pistorius 7
Projective model of human knowledge(acc. to Gerhard Vollmer)
sensation
perception
experience
scientific knowledge
environment
Inpu
tO
utp
ut
Stefan Pistorius 8
Individual world view of a human (or a computer?)= Adaptive network interpretation of the projective model of human knowledge
sensation IATMstransformational rules, sensorial patterns
perception IATMstransformational rules, perceptional patterns
experience IATMstransformational rules, concepts, ordinary facts
scientific knowledge IATMs
transformational rules, concepts, scientific facts
conceptual knowledge
non-conceptual knowledge
environment
Inpu
tO
utp
ut
Stefan Pistorius 9
Individual world view of a humanon the level of an adaptive (neural) network
back
Stefan Pistorius 10
Individual ‘world view of a computer’on the level of a computer chip network
back
Stefan Pistorius 11
World views consist of different ‘knowledge domains’
Knowledge domain:
• the supra-individual content of a particular field of knowledge
• consists of factual knowledge and transformational knowledge
• constituted by one or more agents
Knowledge domain (technical definition):
A network of agents exchanging more messages within their network than with
others
Stefan Pistorius 12
An adaptive network of agents
world view of agent 3world view of agent 2 world view of agent 4world view of agent 1
sensation
perception
experience
scientific knowledge scientific knowledge scientific knowledge scientific knowledge
experience experience experience
perception perception perception
sensation sensation sensation
Stefan Pistorius 13
world view of agent 1 := KD1 + KD2 + KD3 + non-conceptual knowledgeworld view of agent 2 := KD1 + KD2 + KD4 + non-conceptual knowledgeworld view of agent 3 := KD2 + KD3 + KD4 + non-conceptual knowledgeworld view of agent 4 := KD3 + KD4 + KD5 + non-conceptual knowledge
knowledge domain KD1 : agent 1 + agent 2knowledge domain KD2 : agent 1 + agent 2 + agent 3knowledge domain KD3 : agent 1 + agent 3 + agent 4knowledge domain KD4 : agent 2 + agent 3 + agent 4knowledge domain KD5 : agent 4
Knowledge domains established by an adaptive network of agents
world view of agent 3
non-conceptual
knowledge
conceptual knowledge
KD 2 KD 3 KD 4
world view of agent 2
non-conceptual
knowledge
conceptual knowledge
KD 1 KD 2 KD 4
world view of agent 4
non-conceptual
knowledge
conceptual knowledge
KD 3 KD 4 KD 5
world view of agent 1
non-conceptual
knowledge
conceptual knowledge
KD 1 KD 2 KD 3
Stefan Pistorius 14
A fraction of the adaptive global knowledge network
compare neural network and chip network
Stefan Pistorius 15
Knowledge propagation and knowledge evolution
Knowledge propagation:
Knowledge propagates if one IATM outputs a message to an other that
accepts and memorises it as knowledge.
Knowledge evolution:
If the interaction process is disrupted and one party or both parties adapt their
knowledge to be able to exchange messages, we talk about knowledge
evolution.
Stefan Pistorius 16
Facts and rules about knowledge evolution(if you accept the adequacy of the adaptive network model)
• Interaction triggers propagation and evolution of knowledge.
• All knowledge is hypothetical (according to the formal model)
• Knowledge evolves by trial and adaptation on error
Stefan Pistorius 17
Adaptive network interpretation of the EET programme
• KDs develop in evolutionary process• STs develop in evolutionary process
• KDs can be refuted and adapted• STs can be refuted and adapted
• KDs are hypothetical• STs seen as conjectures
• Knowledge Domain (KD)• Scientific Theory (ST)
Adaptive network modelKarl Popper
Stefan Pistorius 18
Adaptive network interpretation of the EET programme
• influence of individual world views on
adaptation processes in KDs
• influence of individual beliefs on
‚consensus practise‘
• message exchange processes within
KDs
• ‚division of cognitive labour‘
Philip Kitcher
• KDs develop in evolutionary process• STs develop in evolutionary process
• KDs can be refuted and adapted• STs can be refuted and adapted
• KDs are hypothetical• STs seen as conjectures
• Knowledge Domain (KD)• Scientific Theory (ST)
Adaptive network modelKarl Popper
Stefan Pistorius 19
Agenda
• Introduction
• An adaptive network model of knowledge and knowledge evolution
• The topology of the global knowledge network and epistemic consequences
• The future of the global knowledge network?
Stefan Pistorius 20
Stefan Pistorius 21
Characteristics of scale-free networks
• degree distribution follows a power law: P(k) ~ k−γ with fraction P(k) of
nodes in the network having k connections (2 < γ < 3)
• preferential attachment: new nodes tend to attach to so-called hubs, i.e.
nodes that are linked to an enormous number of other nodes
the rich get richer!
• examples of scale-free networks: World Wide Web, Internet, molecules in
cellular metabolism, social networks, research collaborations, some “fMRI
networks”, network of knowledge domains(?)
Stefan Pistorius 22
Which are the epistemic consequencesof the scale-free structure of complex networks?
– Knowledge hubs (in our world views, within knowledge domains, in
resarch groups, between research groups, …) dominate and influence
our knowledge evolution.
– Internet hubs like Google, Yahoo, Microsoft and others collect and
distribute data
– hence they decide which knowledge to propagate
– hence they establish a knowledge selection process,
– hence knowledge domains converge!
– Hubs can and will be used to analyse petabytes of data, thus
– they can identify patterns of collective behaviour in nature and
– patterns of collective behaviour in human societies
– hence new knowledge domains evolve, others vanish.
Stefan Pistorius 23
Agenda
• Introduction
• An adaptive network model of knowledge and knowledge evolution
• The topology of the global knowledge network and epistemic consequences
• The future of the global knowledge network?
Stefan Pistorius 24
The future of the global knowledge network?
If interaction in the global knowledge network continues to intensify,
• Knowledge domains will converge more rapidly.
• The evolution of (new) knowledge domains will accelerate.
• The difference between individual and supra-individual knowledge will
dissolve.
• Every single agent (humans and technical devices) will be connected to the
global knowledge network.
• Each agent's perception of the world will be perfectly compatible with all
knowledge (especially scientific knowledge) about the world.
• Every single observation and every single interaction of an agent with
nature (even with her/his/its own physical body) will immediately contribute
to the perception and, if necessary, to the adaptation of the global network.
• The global knowledge network will be better adapted to nature and the
universe.
Stefan Pistorius 25
Thank you for your attention.
Any questions?
Top Related