> LNK#`2$bjbj1<$
"JJJ84$"!h9!;!;!;!;!;!;!$b#h%_!_!t!>
9!
9!
:,8iJ6E
!!H!OR&&&h
_!_!R!"""""""""The option value of learning-by-doing
Karsten Neuhoff and Rutger-Jan Lange
Overview
In the current technology-policy discussion two arguments are frequently made in support of deployment of new technologies. Firstly, the experience that comes with deployment and advances through R&D are said to reduce the costs of the technology. Secondly, it is argued that governments should support a portfolio of new energy technologies. These arguments are usually descriptive in nature and based on case-studies. This paper will combine and quantify the mentioned approaches, with the purpose of providing practical policy guidance.
Although it may be perfectly reasonable to assume that experience through deployment and R&D will reduce the costs, we do not in advance know to what extent. We do therefore not know whether these costs reductions will suffice to make the new technology viable and whether it can make a significant contribution. However, with increasing experience we should at least be in a better position to assess its potential regardless whether that turns out to be more optimistic or more pessimistic. Hence, the idea of learning-by-doing refers not only to the cost reduction of the technology upon deployment, but also to the investor's more accurate expectation of its potential. An evaluation method is necessary, because governments and industry want credible and transparent criteria as to when a technology is abandoned. This reduces regulatory risk for the private sector, and regulatory capture for the government.
The second argument says that investing in a portfolio of technologies decreases the risk associated with individual technologies. It is therefore wise to invest in more than one technology, if the investor wants to maximize the probability that at least one technology is eventually successful. Also, diversification could decrease the dependence on volatile prices and scarce resources. But, given that every added technology diversifies our portfolio, we could ask whether it would be worthwhile to pursue all of them?
This paper will address both the real-options and the portfolio questions, with the purpose of providing practical policy guidance, and rules for when to strategically abandon or invest.
Method
First, for the real-options part, we model the development of a single renewable technology as a stochastic optimal-control problem, where the successful completion of the renewable technology (and the corresponding date) is a random variable. This random variable has a probability distribution that reflects all possible outcomes, and the ranges of its parameters depend on the obtained experience by deployment or alternatively on the R&D efforts of the investor. The investor is rational and Bayesian, in the sense that he updates his expectation of the potential of the technology when he has more information about the learning rate. The idea of learning-by-doing refers not only to the empirical improvement of the technology upon deployment, for which we will use data, but also to the investor's more accurate valuation and expectation of its potential. Therefore, the investor may decide to cut funding for the renewable technology under consideration, or to increase investment efforts along the way, when uncertainty is resolved. This continuous option to adjust investment efforts increases the value of the project.
We anticipate being able to find a closed form solution, perhaps under quite restrictive conditions. However, we will also investigate the problem numerically with more realistic assumptions.
This part of the analysis will provide
the real-options value of strategically adjusting investment levels when sampling the learning curve, taking into account uncertainty
a sensible abandonment policy
With just one renewable technology under consideration, an investor without a budget constraint would simply invest up to the marginal value of the last dollar. However, when the investor pursues a whole portfolio of renewable technologies, and only requires one technology to eventually be successful, then the last dollar could maybe have been spent better. We assume that deployment of the technology leads to an initially accelerating and eventually decelerating accumulation of know-how. The development of this know-how will shed light on the ranges for parameters determining the learning curve, and thus on the technologys final performance. Since also the expectation-value of the project upon completion will change, better knowledge of the parameters of the learning curve will guide changes in the rate of investment which connects with the first part of our analysis. We will use integer optimisation to determine the efficient frontier in terms of risk and expected pay-off. Also, we will consider the effect of adding a single technology to the portfolio, given the stochastic evolution of the learning rates of all technologies in the portfolio.
This part of the analysis will provide
the value of the parallel support of various technologies
a sensible portfolio selection rule
Combining the two parts of our analysis, we will show the trade-off between exploring on the one hand, by investing in many different technologies, and exploiting on the other hand, by focusing resources on a small number of technologies, while taking into account that the learning curve of each technology depends non-trivially on the amount invested in it.
Results
Combining the two parts of our analysis, we will show (i) decision criteria to decide on the continued support of a technology policy where information about the learning rate emerges with increasing investment into the deployment of a technology (ii) how this decision criteria applies to a portfolio of a technology. We anticipate that this will allow us to find a heuristic decision criteria for governments pursing technology policy.
References
Angelis, D.I. 2000. Capturing the option value of R&D. Research-Technology Management, Volume 43, No. 4. 31-34.
Benaroch M. 2001. Option-based management of technology investment risk. IEEE Transactions on Engineering Management, Volume 48, No. 4.
van Benthem A. A., G.J. Kramer, R. Ramer. Forthcoming. An options approach to investment in a hydrogen infrastructure. Energy Policy. Article in Press, Corrected Proof.
Doraszelski, U. 2001. The net present value method versus the option value of waiting: a note on Farzin, Huisman and Kort (1998). Journal of Economic Dynamic and Control, Volume 25, Issue 8. 1109-1115.
Doraszelski, U. 2004. Innovations, improvements, and the optimal adoption of new technologies. Journal of Economic Dynamics and Control, Volume 28, Issue 7. 1461-1480.
Farzin, Y., Huisman, K. and Kort, P. 1998. Optimal timing of technology adoption. Journal of Economic Dynamics and Control 22. 779-799.
Graham A. Davis, Brandon Owens. 2003. Optimizing the level of renewable electric R&D expenditures using real options analysis. Energy Policy Volume 31, Issue 15, December 2003, 1589-1608
Wang, J., W-L. Hwang. Forthcoming. A fuzzy set approach for R&D portfolio selection using a real-options valuation model. Omega Article in Press, Corrected Proof.
!%&';KLMVW P s
t
#0?RWcmɾɺzzrjbh"MCJaJh#'CJaJh{CJaJhyCJaJhS:~CJaJh{!CJaJhNj?CJaJhWVCJaJhyh;Y5CJaJhyhB5CJaJhBhŽŽzzrzh;YCJaJh;YhCJaJhyhB5CJaJhyhWV5CJaJhKCJaJhV\CJaJhho.CJaJh8qCJaJhyCJaJhyCJaJhQd
CJaJh{!CJaJhICJaJhX&CJaJhBCJaJh;Yh{!CJaJ( RYl&[qrtu-:"FGI
%Ǳ꼩ǡꎃꩡ{shCJaJhCJaJh;YhC;CJaJh;YhvCJaJhCJaJh:CJaJhS:~CJaJh;YhAE9CJaJh;YhF_CCJaJh8jCJaJh;YhmrcCJaJh
CJaJhyCJaJh;YhCJaJh;YhCJaJ-
2cdTI
&FEƀgf-gdjigdjigd#7
&Fgd;YI
&FEƀgf-gd:gd;Y%)*12$&*56<o6<?MWXZj !@NgoʺºպʲʺʪՇhSECJaJhCJaJh6CJaJh;YhDCJaJh-IICJaJh.}CJaJh;KCJaJhS:~CJaJh
CJaJh;YhCJaJh;YhAE9CJaJh#7CJaJh:CJaJhCJaJh2 CJaJ/$*bc6@ST\]!" @ ŽŽŽŴؠskc[LhN{hN{CJaJmHsHhBCJaJhzCJaJhWVCJaJh:hB5CJaJhji5CJaJh;KCJaJhO=XCJaJh2 CJaJhCJaJhh5CJaJh5CJaJhN2CJaJhQd
hCJaJhjiCJaJhSECJaJhIXCJaJhCJaJhCJaJh8jCJaJST\]!"^GC$Eƀtfgd;Ygd;Ygdgd#7I
&FEƀgf-gdji
" !!:";"""###$$$&$'$)$*$,$-$/$0$1$2$gd
fgdgd;Ygdz@ G ;"`"f"""~#####$$$%$'$($*$+$-$.$1$2$ʾ٫ّ~~~~h]jh]Uh
fh
fhzhzCJaJmHsHh;YhzCJaJhzCJaJhhCJaJhCJaJmHsHhhCJaJmHsHhCJaJhN{CJaJhN{hN{CJaJhN{CJaJmHsH/0P/ =!"#$%F@F StandaardCJ_HaJmH sH tH LA@LStandaardalinea-lettertypeZi@ZStandaardtabel :V44
la.k@.
Geen lijstbObDefault7$8$H$-B*CJOJQJ^J_HaJmH phsH tH >@>
VoetnoottekstCJaJ>&>VoetnootmarkeringH*6U@!6 Hyperlink>*B*phFV@1FGevolgdeHyperlink>*B*phHQ@BHPlatte tekst 3$dha$CJ>B@R>Platte tekst$a$CJjC@bjPlatte tekst inspringen$hdh^h`a$CJe@rHTML - vooraf opgemaakt7
2(
Px4 #\'*.25@9)B*CJOJQJ^JaJmHphsHtHF@FBallontekstCJOJQJ^JaJ2<&'LMWXuv
H
I
2cdST\]!":;#$&')*,-/0300000000000000000000 0 000@0 0 00000000000000000@000000000@000A@000A@000A@000A00A&'LMWXuv
H
I
2cdST\]!":;#300000000000000000000 00000$@0@0@ 0@ 000@0@00000000000000000000000 m%@ 2$
"2$1$8@0(
B
S ?'.!G(.07<@;f$$&&'')*,-/03ks"5,GST/9;Gf$$&&'')*,-/0333333333333333333MXT]":C#$$&&'')*,-/03$$&&'')*,-/03k [$]5^`CJo(()^`.pLp^p`L.@@^@`.^`.L^`L.^`.^`.PLP^P`L.^`OJPJQJ^Jo(-^`OJQJ^Jo(hHopp^p`OJQJo(hH@@^@`OJQJo(hH^`OJQJ^Jo(hHo^`OJQJo(hH^`OJQJo(hH^`OJQJ^Jo(hHoPP^P`OJQJo(hHk [Vub XWlTSQd
N{.};YvFC8jA 2 q%#'ho./N2H8AE9:Nj?F_C`DSE-IIKUK`QWVO=XIX]!_`?@ABDEFGHIJMRoot Entry F/8iOData
1Table'&WordDocument1<SummaryInformation(;DocumentSummaryInformation8CCompObjq
FMicrosoft Office Word-document
MSWordDocWord.Document.89q