13. Kitaro Nishida, Japanese philosopher, proposed the concept of ’Pure
Experience’. Pure experience is an exact experience without clearly
distinguishing its subject and object. (in 1921)
Kitaro Nishida
(1870 – 1945)
Observation
from the outside
Pure Experience
For example, experiencing the feeling of “what a beautiful flower!”
should happen before the understanding of "I (as subject) am looking
at this flower (as object), which is beautiful”. In other words, when
experiencing something, it is always beyond the dichotomy of
subject and object.
‘I’
subject ‘flower’
object
純粋経験
15. xn+1 = a xn ( 1 - xn )
Diverse complex patterns can emerge
even in the universe governed by deterministic laws.
16. Control Parameter: ranging from µ = 0 to µ = 1 xn+1 = 4µ xn ( 1 - xn )
Round-Up into the decimal place, d = 3
The state-transition networks for the logistic map
19. Takashi Iba, "An Autopoietic Systems
Theory for Creativity”, Procedia -
Social and Behavioral Sciences, Vol.2,
Issue 4, 2010, pp.6610-6625
Collaborative Innovation Networks Conference 2009
Procedia
Social and
Behavioral
Sciences
www.elsevier.com/locate/procedia
COINs2009: Collaborative Innovation Networks Conference
An Autopoietic Systems Theory for Creativity
Takashi Ibaab
aMIT Center for Collective Intelligence, Cambridge MA, USA
bFaculty of Policy Management, Keio University, Japan
AAbbssttrraacctt
In this paper, a new, non-psychological and non-sociological approach to understanding creativity is proposed.
The approach is based on autopoietic system theory, where an autopoietic system is defined as a unity whose
organization is defined by a particular network of production processes of elements. While the theory was
originally proposed in biology and then applied to sociology, I have applied it to understand the nature of
creation, and called it "Creative Systems Theory". A creative system is an autopoietic system whose element
is "discovery", which emerges only when a synthesis of three selections has occurred: "idea", "association",
and "consequence". With using these concepts, we open the way to understand creation itself separated from
psychic and social aspects of creativity. On this basis, the coupling between creative, psychic, and social
systems is discussed. I suggest, in this paper, the future of creativity studies, re-defining a discipline
"Creatology" for inquiring creative systems and propose an interdisciplinary field as "Creative Sciences" for
interdisciplinary connections among creatology, psychology, and so on.
Keywords; creativity; systems theory; autopoiesis; pattern language
11.. IInnttrroodduuccttiioonn
In this paper, a new, non-psychological and non-sociological approach to understanding creativity
is proposed. The approach is based on autopoietic system theory, where an autopoietic system is
defined as a unity whose organization is defined by a particular network of production processes
of elements. While the theory was originally proposed in biology and then applied to sociology, I
have applied it to understand the nature of creation, and called it "Creative Systems Theory". A
creative system is an autopoietic system whose element is "discovery", which emerges only when a
創造システム理論
Creative Systems Theory
20. Iba, T. (2009) “An Autopoietic Systems Theory for Creativity”, COINs2009.
創造 とは “発見”の生成・連鎖である
29. xn+1 = a xn ( 1 - xn )
Diverse complex patterns can emerge
even in the universe governed by deterministic laws.
30. ChaoticWalker
Iba, T. & Shimonishi, K. (2011), "The Origin of Diversity: Thinking with Chaotic Walk," in Unifying Themes in Complex Systems
Volume VIII: Proceedings of the Eighth International Conference on Complex Systems, New England Complex Systems Institute
Series on Complexity (Sayama, H., Minai, A. A., Braha, D. and Bar-Yam, Y. eds., NECSI Knowledge Press, 2011), pp.447-461.
32. d =1 d =8 d =16
•A system consisting of the finite number of possible states
eventually exhibits periodic cycle.
•To tune this parameter means to vary the degree of chaotic behavior.
regular irregular
all patterns are generated with a =3.76 (in the chaotic regime)
33. Building state-transition networks
the set of state
transitions
building networks
To connect each state into its successive state
State-transition network is a “map” of the
whole behavior of the system, so one can
take an overview from bird’s-eye view.
f
xn+1 = a xn ( 1 - xn )
Takashi Iba, "Hidden Order in Chaos: The Network-Analysis Approach To Dynamical Systems", Unifying Themes
in Complex Systems Volume VIII: Proceedings of the Eighth International Conference on Complex Systems,
Sayama, H., Minai, A. A., Braha, D. and Bar-Yam, Y. eds., NECSI Knowledge Press, pp.769-783, 2011
34. Control Parameter: µ = 1 Therefore, xn+1 = 4 xn ( 1 - xn )
Round-Up into the decimal place, d = 4 Therefore, 10001 states
The state-transition networks for the logistic map
xn+1xn
0.0000 0.00000000 0.0000
0.0001 0.00039996 0.0004
0.0002 0.00079984 0.0008
0.0003 0.00119964 0.0012
0.0004 0.00159936 0.0016
0.0005 0.00199900 0.0020
0.0006 0.00239856 0.0024
0.0007 0.00279804 0.0028
0.0008 0.00319744 0.0032
0.0009 0.00359676 0.0036
0.0010 0.00399600 0.0040
0.0011 0.00439516 0.0044
0.0012 0.00479424 0.0048
0.0013 0.00519324 0.0052
0.0014 0.00559216 0.0056
0.0015 0.00599100 0.0060
0.0016 0.00638976 0.0064
f h
35. Control Parameter: ranging from µ = 0 to µ = 1 xn+1 = 4µ xn ( 1 - xn )
Round-Up into the decimal place, d = 3
The state-transition networks for the logistic map
36. The dashed line has slope -2.
The networks are scale-free networks with the degree exponent γ = 1,
regardless of the value of the parameter µ.
from µ = 0.125 to 1.000 by 0.125 in the case d = 7
The cumulative in-degree distributions of state-transition
networks for the logistic map