andrzej m.j. skulimowski decision · the basic decision units that may occur in a hybrid...
TRANSCRIPT
![Page 1: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/1.jpg)
Applying Anticipatory Networks to Scenario Planning and Backcasting in Technological Foresight
Andrzej M.J. Skulimowski
Decision Sciences Department, AGH University of Science & Technology, Kraków, Poland International Centre for Decision Sciences and Forecasting Progress & Business Foundation
5th FTA Conference, Brussels November 27-28, 2014
![Page 2: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/2.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Organisation of this talk:
1. Motivations: complex decision-making problems for middle- and long-term strategic planning
2. Future exploration methodology: forecasting, foresight, scenarios, planning, backcasting, roadmapping
3. Technological evolution as a modelling background
4. An introduction to modelling the future with anticipatory networks
5. Network algorithms for reverse planning
6. Anticipatory networks as superanticipatory systems
7. Hybrid anticipatory networks
8. Anticipatory modelling process: from scenario planning to roadmapping
9. Case study: Anticipatory planning of operations of a Regional Creativity Support Centre
10. Final Conclusions and Remarks
![Page 3: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/3.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Motivations:
Practice-oriented:
o Strategic planning: a need to fuse and implement the quantitative
results of foresight, normative scenarios, and impact analysis
o Better exploration of foresight outcomes
o Analysis of sustainability problems: multicriteria decisions for better future, environmental models for sustainability assessment
o Filtering ICT/AI development scenarios – only those nondominated remain
Theory-oriented:
o Vector-valued utilities in group decision-making
o Multi-step Stackelberg games, multicriteria n-level programming
o Discrete event systems control in causal networks with feedback
![Page 4: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/4.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Multicriteria Optimisers
A scheme of operation of a simple multicriteria optimiser O corresponding to the problem
(F:U→E)→min(P) with the solution set Π(U,F,P) (MO)
O may be endowed additionally with an interactive selection mechanism
ψ: Π(U,F,P) x I → Π(U,F,P),
where Π(U,F,P) is the set of nondominated points for the problem (MO) and I is the set of external states of knowledge. ψ models the selection of a unique compromise solution from the set of nondominated solutions
X(F,U,P)
F(X(F,U,P)
OPTIMIZER
O=X(F,U,P)
F
U
P
![Page 5: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/5.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Networks of Optimizers: Simple Chains
Linked multicriteria optimizers:
O1 →φ(1) O2 → φ(2) O3→ φ(3) … → φ(n-1) On (2),
Causality model:
a) linking functions φ force the choice of a solution in subsequent problems
b) linking functions φ influence the sets Ui only
c) φ can also influence the choice of criteria Fi and the selection rules ψ
![Page 6: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/6.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
N-level model of consequences
The decision process: ui(ui-1) for i=1,..N, :=Yi Fi-1
![Page 7: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/7.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Anticipatory decision problems in networks of optimisers
Assumptions:
1. Solution to an optimisation problem Op may influence Mp independent subsequent problems
2. An optimisation problem Or may be influenced by Nr preceding problems
3. The influence aggregation rules can be defined for each influenced optimiser (e.g. as intersection of the sets of feasible alternatives, each one imposed by a different preceding optimiser)
Explorative Scenarios and Forecasts can be used simultaneously
to support decisions, when the decisions concern a middle- or long-term future. Scenarios can be external-events-driven, when included in solution models, they allow to generate decision rules
![Page 8: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/8.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Problem formulation for the networks – anticipatory feedback with the future
Definition 1. Let Oi i=0,1,…,N, be a chain of optimizers with causal relations given by the restrictions of the scope of admissible decisions in Ui+1 such that if ui is an admissible decision for the optimizer Oi then ui+1 is an admissible decision in Ui+1 iff ui+1(i)(ui), where φ(i):=Y(i) Fi-1 is a multifunction describing the restrictions on admissible choice of solutions in optimization problems subsequent to Oi. Moreover, let us assume that all elements of U0 are admissible. Any sequence of admissible solutions (u0,m(0), u1,m(1),…, uN,m(N)) will be called an admissible chain.
Definition 2. For a chain of optimizers Oi i=0,1,…,N the anticipatory feedback condition (FC) is defined as the requirement that for
jJ:= {J(i):i=0,…,N-1} and certain family of subsets {Vi,j}jJ.
the feedback at Oi is realized so that the decision-maker at Oi strives to select the solution which guarantees that the the solutions selected at a future decision node Oj belong to {Vi,j}jJ for all jJ(i).
![Page 9: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/9.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Scenario Filtering Problems Problem 1. Find the set of all admissible chains that fulfil additionally
the anticipatory feedback condition FC (i.e. anticipatory scenarios).
A problem with the relaxed feedback condition:
Problem 2. Find the set of all admissible chains that maximize the following function:
g(u0,…,uN):=iJ(0)h(ui, q(i))w0,i (RFC1)
such that for all i, 1i<N, the truncated admissible chain (ui,…,uN) maximizes the function
g(ui,…,uN):=jJ(i)h(uj, q(j))wi,j (RFC2)
where h is a quantitative measure of satisfaction of FC , e.g.
h(ui,q(i)):=║Fi-1(ui)-q(i)║,
and wi,j are positive relevance coefficients for the feedback relation between the optimizers Oi and Oj.
Problem 3. Find the set of all admissible chains that fulfill the feedback condition or the conditions RFC1,2 and start at a nondominated point at U0
![Page 10: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/10.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Anticipatory Networks and Causality
Definition 3. A scenario which is a solution to Problem 3 will be called an anticipatory scenario
Definition 4. Any two optimizers Om=X1(U,F,P) and On=X2(W,G,R) are in the causal influence relation if there exist two different outputs from Om, x1,x2X1, such that either the choice in On is restricted to two different subsets of W that depend on choice of x1 or x2 in Om, or if the solution selection rule R or the criterion G are modified in different manner depending on the choice of x1 or x2.
Definition 5. An anticipatory network (of optimizers) is a causal network (i.e. network with at least two optimizers in the causal influence relation with at least one additional anticipatory feedback relation.
![Page 11: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/11.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
A computational example (1): a chain of five multicriteria
optimization problems (Ui,Fi)
![Page 12: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/12.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
A computational example (2)
The multifuctions (i) are defined explicitly by showing allowed choices
![Page 13: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/13.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Networks of Optimizers – Anticipatory Trees
A tree of optimizers with future feedback consisting of 10 elements Oi=(Ui,Fi,Pi), i=0,1,…9, and four chains, with Fi:=idUi, causal relations defined by multifunctions Yi,
and seven essential future feedback relations (solid arrows). The dotted arrow between U3 and U9 is an irrelevant anticipatory feedback (no causal relation).
![Page 14: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/14.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Anticipatory Networks in the general case
![Page 15: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/15.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Hybrid Anticipatory Networks – new units
The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced by the states of nature N, triangle (b) - the pre-determined algorithmic decision unit, where the decision may additionally depend on the states of nature N, rounded box (c) – random decision is selected based on known inputs and an output distribution function, subdivided box (d) – 2-player non-cooperative game unit
![Page 16: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/16.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
A Hybrid Anticipatory Network
![Page 17: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/17.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Further applications: Backcasting and sustainability
The common problem: how to reach a desired state in the future
Anticipatory feedback models Backcasting
Source: http://innovatechange.co.nz
![Page 18: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/18.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Standard backcasting vs. anticipatory networks
Specification A typical backcasting procedure Anticipatory modelling
Common goals ● To assess the feasibility of given visions of the future
● To support strategic planning projects that involve multiple strategies
Time scale Determine the reverse planning horizon based on information provided by experts and stakeholders
In addition to expert information: AN allows the modeller to determine the maximum grade of the network and derive the anticipation horizon out of it
Core target analysis
Define future ideal states (normative scenarios), and feasible actions that may be applied to reach them
Define the criteria and preference structures as well as reference points for relaxing anticipatory feedback conditions
Strategic planning
Engage experts and stakeholders to take part in the action planning. Use panels, workshops, brainstorming, etc. to elicit their opinions
Use AN algorithms to calculate compromise solutions along anticipatory chains. The information provided while building the AN generates the solution
Interaction All steps in the procedure should be repeated with updated information and forecasts until the preference thresholds set by stakeholders and decision makers are met
Once built, the AN remains stable during the analysis. The interaction touches upon the presentation of different nondominated anticipatory chains until the present-time decision makers are satisfied
![Page 19: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/19.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Case Study: Anticipatory planning for a Regional Creativity Support Centre
The case studied within the foresight project SCETIST, www.ict.foresight.pl
![Page 20: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/20.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
Stage II: Long-term planning
Case Study: Anticipatory planning for a Regional Creativity Support Centre
The case studied within the foresight project SCETIST, www.ict.foresight.pl
![Page 21: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/21.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
References:
R. Rosen (1985). Anticipatory Systems - Philosophical, Mathematical and Methodological Foundations, Pergamon Press, London (2nd Edition, Springer, 2012).
A.M.J. Skulimowski (1985). Solving Vector Optimization Problems via Multilevel Analysis of Foreseen Consequences. Found. Control Engrg., 10, No. 1, 25-38. (available from www.researchgate.net)
A.M.J. Skulimowski (2012). Hybrid Anticipatory Networks. 11th ICAISC, Zakopane 2012, LNAI 7268, Springer, pp.706-715, http://link.springer.com/chapter/10.1007/978-3-642-29350-4_83
A.M.J. Skulimowski (2013). Exploring the future with anticipatory networks, AIP Conf. Proc. 1510, pp. 224-233; doi: http://dx.doi.org/10.1063/1.4776525
A.M.J. Skulimowski (2014). Anticipatory Network Models of Multicriteria Decision-Making Processes. Int.J.Systems Sci, 45(1), 39-59, http://dx.doi.org/10.1080/00207721.2012.670308
![Page 22: Andrzej M.J. Skulimowski Decision · The basic decision units that may occur in a hybrid anticipatory network: simple box (a) - multiple-input optimizer with output possibly influenced](https://reader030.vdocuments.site/reader030/viewer/2022011813/5e3b9a98e9dc0b6af622d193/html5/thumbnails/22.jpg)
THEME 3: CUTTING EDGE FTA APPROACHES
THANK YOU FOR YOUR ATTENTION!
INQUIRIES: ams (at) agh.edu.pl, www.foresight.pl
This research has been supported within the foresight project No. WND-POIG.01.01.01-00-021/09: “Scenarios and development trends of selected
information society technologies until 2025” (SCETIST) funded by the ERDF within the Innovative Economy Operational Programme, 2006-2013