Value of flexibility in funding
radical innovations
E. Vilkkumaa, A. Salo, J. Liesiö, A. Siddiqui
EURO INFORMS Joint meeting, Rome, Jul 1st-4th 2013
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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 ʻastronomic revenuesʼ.”
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?
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.
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
More to
accurate
launching
• Prior
Projects
with
future
Projects’
future
estimates
canthe
be
the
projects,
values
in
the
top
cannot
obtained
later
DM
observes
1%
are
assumed
observed
by the to
uncertain
estimates
yield
DM additional,
about these
future
indirect
benefits
values
after
having been
completed
Top 1%
f(v)
Model and assumptions: project selection
• Decision setting in each period:
–
–
–
–
B
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
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
Period 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 ce, and
– Abandon projects which seem unpromising to release resources for
new opportunities
Launch new projects
B
On-going projects
and evaluation costs
Projects launched
in period t-T
completed
Some of the projects
launched in period t-q
abandoned
Period t
Launch new projects
On-going projects
and evaluation costs
Projects launched
in period t+1-T
completed
Some of the projects
launched in period
t+1-q abandoned
Period t+1
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) +ce∙CF ≤ B
Conditionally funded projects
that
Unit-cost projects with full funding
Evaluation
costs
Conditionally funded unithave
been
continued
based
on
that have not yet been completed
cost projects that have not
the re-evaluation
yet been re-evaluated
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
Radical innovations
Average portfolio value
1
1
R = rejected projects
C = continued projects
R
0.6
0.4
A
CF
C
C
C
FF
FF
FF
FF
1
2
3
4
0.2
0
0.8
Share of projects
Share of projects
0.8
Period
R = rejected projects
C = continued projects
R
0.6
A
0.4
CF
0.2
C
0
1
2
C
C
3
4
Period
• 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
Optimal funding policy for radical
innovations
High initial uncertainty
Low initial uncertainty
1
0.8
1
R
0.8
Share of projects
Share of projects
R
0.6
A
0.4
CF
0.2
0
0.6
0.4
0.2
1
C
C
C
2
3
4
Period
0
A
CF
CF
1
2
C
C
3
4
Period
•
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
Cross-comparison of optimal policies
• 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
Policy 1
Policy 2
Average project value
21.37
24.25
Average portfolio value
641
267
Expected share of missed radical innovations
29%
16%
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