Value of flexibility in funding radical innovations

1. Value of flexibility in funding radical innovations E. Vilkkumaa, A. Salo, J. Liesiö, A. Siddiqui EURO INFORMS Joint meeting, Rome, Jul 1st-4th 2013 The document can be stored and made available to the public on the open internet pages of Aalto University. All other rights are reserved.

2. Project portfolio selection • A pervasivedecisionproblem • R&D projectselection in privatecompanies • Public funding of researchprojects • Projectsaretypicallyselected with the aim of maximizing the average portfolio value

3. Funding radical innovations • However: • Kahneman (2011): “The goal of venture capitalists is to be able to predict correctly that a start-up is going to be extremely successful, even at the cost of overestimating the prospects of many other ventures.” • Kanniainen (2011): “The purpose of public R&D subsidies is not to increase the average success of the subsidized firms, but to find those few innovation ideas out of many that ultimately result in ʻastronomic revenuesʼ.” Whatkinds of projectevaluation and selectionpoliciespromoteradicalinnovations, defined as projects with extremelyhighvalues? How dothesepoliciesdifferfromthosethatmaximizeaverage portfolio value? Kahneman, D., (2011). Thinking, Fast and Slow, Farrar, Straus and Giroux, New York. Kanniainen, V., (2011). The tragedy of false rejections: should society subsidize R&D projects? (Title translated from Finnish by E. Vilkkumaa) The Finnish Economic Papers, Vol. 24, pp. 461-473.

4. Model and assumptions: value • Projects are selected based on their future values, which are realizations from prior distribution f(v) • Radical innovations are modeled as projects with exceptionally high future values, e.g., in the top 1% of f(v) • Such projects are assumed to yield additional benefits after the project itself has been completed through, e.g., commercialization

5. Model and assumptions: uncertainty • More accurate estimates can be obtained later • Prior to launching the projects, the DM observes uncertain estimates about these future values • Projects with future values in the top 1% are assumed to yield additional, indirect benefits after having been completed • Projects’ future values cannot be observed by the DM

6. Model and assumptions: project selection • Decision setting in each period: • Fixed budget B • n new projects available with unit cost • Projects selected based on uncertain estimates of their future value • Future value will be realized if project is funded for T periods Launch new projects Launch new projects Launch new projects On-going projects On-going projects On-going projects B Projects launched in period t -T completed Projects launched in period t+1-T completed Projects launched in period t+2-T completed Period t+2 Period t Period t+1

7. Model and assumptions: flexibility • Estimates about future value become more accurate in time → the DM may benefit from the flexibility to • Re-evaluate some projects after q < T periods at cost ce, and • Abandon projects which seem unpromising to release resources for new opportunities Launch new projects Launch new projects On-going projects and evaluation costs On-going projects and evaluation costs B Some of the projects launched in period t-q abandoned Some of the projects launched in period t+1-q abandoned Projects launched in period t-T completed Projects launched in period t+1-T completed Period t Period t+1

8. Funding policy • Fundingpolicy (FF,CF,A,q) for each set of n new projects • FF: # of projectsthataregrantedfullfunding • CF: # of projectsthatarefundedconditionally and re-evaluatedafterq periods • A: # of projectsthatareabandonedbased on the re-evaluation • q: re-evaluation & abandonmenttime • Policyselectedsubject to budgetconstraint • T∙FF + q∙CF + (T-q)∙(CF - A) +ce∙CF≤ B Conditionally funded projects that have been continued based on the re-evaluation Unit-cost projects with full funding that have not yet been completed Evaluation costs Conditionally funded unit-cost projects that have not yet been re-evaluated

9. Funding policy • Which funding policies yield most value over time, when the objective is to either • Maximize the sum of the selected projects’ expected future values, or to • Maximize the expected share of funded projects among those with future values in the top 1%, i.e., the radical innovations?

10. Optimal funding policies Radical innovations Average portfolio value R = rejected projects C = continued projects R = rejected projects C = continued projects • To maximize average portfolio value: full funding to many projects, abandon only a small share • To fund radical innovations: launch many projects, re-evaluate all of them, and abandon a large share

11. Optimal funding policy for radical innovations High initial uncertainty Low initial uncertainty • The more uncertain the initial estimates, the longer the DM should wait before abandoning projects • Fewer projects can be launched and completed → a trade-off between (i) completing more projects, and (ii) waiting for more accurate value information

12. Cross-comparison of optimal policies • Policy 1 (maximizes the average portfolio value): • Full funding for 30 out of 100 projectproposals • Policy 2 (maximizes the share of fundedradicalinnovations): • Conditionalfunding for 48 out of 100 projectproposals • Allre-evaluatedafter 2 periods • 37 of the re-evaluatedprojectsabandoned, 11 completed

13. Conclusions • Significant differences between optimal funding policies for different objectives: • To maximize average portfolio value: long-term commitment to projects based on initial evaluation • To fund radical innovations: launch many projects, re-evaluate all of them, and abandon a large share (ʻup or outʼ) • Policies that are optimal for funding radical innovations can seem cost-inefficient in short term