Value of flexibility in funding radical innovations E. Vilkkumaa, A. Salo, J. Liesiö, A. Siddiqui EURO INFORMS Joint meeting, Rome, Jul 1 st -4 th 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.

Project portfolio selection A pervasive decision problem –R&D project selection in private companies –Public funding of research projects Projects are typically selected with the aim of maximizing the average portfolio value

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 ʻ astronomic revenues ʼ.” 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 What kinds of project evaluation and selection policies promote radical innovations, defined as projects with extremely high values? How do these policies differ from those that maximize average portfolio value?

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

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

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 On-going projects B Launch new projects Projects launched in period t -T completed Projects launched in period t+1-T completed Projects launched in period t+2-T completed Launch new projects Period tPeriod t+1 Period t+2

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 c e, and –Abandon projects which seem unpromising to release resources for new opportunities On-going projects and evaluation costs B Launch new projects Projects launched in period t-T completed Period t Period t+1 Some of the projects launched in period t-q abandoned Launch new projects Projects launched in period t+1-T completed Some of the projects launched in period t+1-q abandoned On-going projects and evaluation costs

Funding policy Funding policy (FF,CF,A,q) for each set of n new projects –FF: # of projects that are granted full funding –CF: # of projects that are funded conditionally and re-evaluated after q periods –A: # of projects that are abandoned based on the re-evaluation –q: re-evaluation & abandonment time Policy selected subject to budget constraint T∙FF + q∙CF + (T-q)∙(CF - A) +c e ∙CF ≤ B Unit-cost projects with full funding that have not yet been completed Conditionally funded unit- cost projects that have not yet been re-evaluated Conditionally funded projects that have been continued based on the re-evaluation Evaluation costs

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

Optimal funding policies Average portfolio value 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 Radical innovations R = rejected projects C = continued projects R = rejected projects C = continued projects

Optimal funding policy for radical innovations 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 Low initial uncertainty High initial uncertainty

Cross-comparison of optimal policies Policy 1Policy 2 Average project value Average portfolio value Expected share of missed radical innovations29%16% Policy 1 (maximizes the average portfolio value): –Full funding for 30 out of 100 project proposals Policy 2 (maximizes the share of funded radical innovations): –Conditional funding for 48 out of 100 project proposals –All re-evaluated after 2 periods –37 of the re-evaluated projects abandoned, 11 completed

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 ( ʻ up or out ʼ ) Policies that are optimal for funding radical innovations can seem cost-inefficient in short term