andrzej m.j. skulimowski decision · the basic decision units that may occur in a hybrid...

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

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


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


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


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


N-level model of consequences

The decision process: ui(ui-1) for i=1,..N, :=Yi Fi-1


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


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).


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


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.


A computational example (1): a chain of five multicriteria

optimization problems (Ui,Fi)


A computational example (2)

The multifuctions (i) are defined explicitly by showing allowed choices


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).


Anticipatory Networks in the general case


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


A Hybrid Anticipatory Network


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


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


Case Study: Anticipatory planning for a Regional Creativity Support Centre

The case studied within the foresight project SCETIST, www.ict.foresight.pl


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


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


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

andrzej m.j. skulimowski decision · the basic decision units that may occur in a hybrid...

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