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

s-pistorius@t-online.de

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?

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Initial question

How can we understand the impact of information technology and theInternet on the evolution of human knowledge?

Can evolutionary epistemology give answers?

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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!

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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?

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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!

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Projective model of human knowledge(acc. to Gerhard Vollmer)

sensation

perception

experience

scientific knowledge

environment

Inpu

tO

utp

ut

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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

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Individual world view of a humanon the level of an adaptive (neural) network

back

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Individual ‘world view of a computer’on the level of a computer chip network

back

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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

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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

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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

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A fraction of the adaptive global knowledge network

compare neural network and chip network

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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.

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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

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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

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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

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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?

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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(?)

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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.

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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?

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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.

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Thank you for your attention.

Any questions?