Abstract
This paper discusses a novel argument to interpret the importance of thinking of collaborative partnerships in precompetitive agreements. To do so, we adopt a dynamic iterative process to model technology diffusion between the partners of an agreement. We find that the success of an agreement of a given length hinges around identifying the suitable efficient combinations of the initial technological endowments of partners. As the time horizon of the agreement expands, the probability of identifying a suitable partner decreases, thus justifying the prevalence of shorthorizon R&D agreements.
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Note that we assume ξ _{ i }>0. This is so because ξ _{ i }=0 would mean that firm i has already exhausted its possibilities to lower costs, thus preemptying its participation in any agreement.
As it appears, for instance, in the Operating procedures of the HDP Inc at http://www.hdpug.org/sites/all/files/Documents/HDP_Op_Procedures_approved_1006.pdf.
As we will argue in the next section, in that case our results would be even more stringent because of the structure of our approach.
We are implicitly assuming the absence of any kind of freeriding behavior, as anticipated at the beginning of this section.
Examples of these partnerships are agreements like Chamalon, Aider, or Chauffeur involving partners form the rubber, automobile, aircraft, electronics, and telecom industries among others.
We do not explicitly model punishments for deviations from the agreements. This would go beyond the main objective of the analysis. Remember that we are assuming that the model satisfies the condition for an optimal contract to exist. Accordingly, the design of the contract already takes into account those penalties.
In general, this is the kind of costbenefit analysis that firms carry out when they evaluate the convenience of joining an agreement. Firms look at the evolution of profits over a finite horizon from the actual situation by computing the present (discounted) value of the flow of future profits. In addition, we compare stock variables at different moments in time and implicitly discount them at the same rate. It is important to remember that we are considering extreme cases where the agreement must be profitable during every single period. Milder assumptions would consider comparing aggregate discounted profits over a certain number of periods. Then opportunities for successful collaboration should appear more easily.
Remember that firms can exploit the benefits they get from the agreement only at the end of period two.
Carsense is a consortium of 12 European car manufacturers, suppliers and research institutes, sponsored by the EC to develop a sensor system, that shall give sufficient information on the car environment at low speeds in order to allow low speed driving. See http://www.carsense.org. Cartalk is another consortium of 7 European car manufacturers, suppliers and research institutes, sponsored by the EC and focusing on new driver assistance systems based on intervehicle communication. See http://www.cartalk2000.net.
References
Aghion Ph, Dewatripont M, Rey P (2002) On partial contracting. Eur Econ Rev 46:745–753
Baptista R (1999) The diffusion of process innovations: a selective review. Int. J. Econ. Bus. 6:107–129
Blackmore D, Rahman A, Shah J (2009) Discrete modeling and analysis of the RS flipflop circuit. Chaos Solitons Fractals 42:951–963
Canepa A, Stoneman P (2004) Comparative international diffusion: patterns, determinants and policies. Econ. Innov. New Technol. 13:279–298
Chesbrough HW (2003) The era of open innovation. MIT Sloan Manag Rev 44:35–41
Chipman JS (1970) External economies of scale and competitive equilibrium. Q J Econ 84:347–385
De Palma A, Droesbeke JJ, Lefevre C (1991) Modèles de diffusion en marketing. Presse Universitaire de France, Paris
Devaney RL (1985) An introduction to the chaotic dynamical systems. Benjamin/Cummings, Menlo Park
Frankort H, Hagedoorn J, Letterie W (2012) R&D partnership portfolios and the inflow of technological knowledge. Ind Corp Change 21:507–537
Gillier Th, Kazakci AO, Piat G (2012) The generation of common purpose in innovation partnerships. Eur J Innov Manag 15:372–392
Hagedoorn J (2002) Interfirm R&D partnership: an overview of major trends and patterns since 1960. Res Policy 31:477–492
Harrigan KR (1986) Managing for joint venture success. Lexington Books, Lexington
Jayaraman V, Bhatti MI, Saber H (2004) Towards an optimal testing of an hypothesis based on dynamic technology transfer model. Appl Math Comput 147:115–129
Karshenas M, Stoneman P (1993) Rank, stock, order and epidemic effects in the diffusion of new process technology. Rand J Econ 24:503–519
Kraft RL (1999) Chaos, Cantor sets and hyperbolicity for the logistic maps. Am Math Mon 105:400–409
Li TY, Yorke J (1975) Period three implies chaos. Am Math Mon 82:985–992
Lokshin B, Hagerdoorn J, Letterie W (2011) The bumpy road of technology partnership: understanding causes and consequences of partnership malfunctioning. Res Policy 40:297–308
Mansfield E (1961) Technical change and the rate of imitation. Econometrica 29:741–766
May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261:459–467
Narula R, Hagedoorn J (1999) Innovating through strategic alliances: moving towards international partnership and contractual agreements. Technovation 19:283–294
Nicolini R, Artige L (2008) Investigación y Desarrollo más Innovación: Recomendaciones para políticas públicas más cercanas a las necesidades de las empresas. CES Edition, Madrid
PérezCastrillo D, Sandonís J (1996) Disclosure of knowhow in research joint ventures. Int J Ind Organ 15:51–75
Roijakkers N, Hagedoorn J (2006) Interfirm R&D partnering in pharmaceutical biotechnology since 1975: trends, patterns and networks. Res Policy 35:431–446
Segrestin B (2005) Partnering to explore: the RenaultNissan Alliance as a forerunner of new cooperative patterns. Res Policy 35:657–672
Singh N, Vives X (1984) Price and quantity competition in a differentiated duopoly. Rand J Econ 15:546–554
Stoneman P (1981) Intrafirm diffusion, Bayesian learning and profitability. Econ J 91:375–388
UNCTAD (2005) World investment report 2005: Transnational corporation and internationalization of R&D, Geneva
Veugelers R, Kesteloot K (1994) On the design of stable joint ventures. Eur Econ Rev 38:1799–1815
Vives X (1999) Oligopoly pricing. Old ideas and new tools. The MIT Press, Cambridge
Zeller Ch (2004) North Atlantic innovative relations of Swiss pharmaceuticals and the proximities with regional biotech arenas. Econ. Geogr. 80:83–111
Acknowledgements
We are grateful to R. Devaney, X. Jarque, D. PérezCastrillo, J. Sandonís, two anonymous referees, and participants at several conferences and seminars for their useful suggestions and discussions. We gratefully acknowledge the financial support from research projects 2009SGR169, ECO20097616, and ConsoliderIngenio 2010 (Xavier MartinezGiralt), 2009SGR00600 and SEJ200801850/ECON (Rosella Nicolini). Xavier MartinezGiralt is a research fellow of MOVE (Markets, Organizations and Votes in Economics). The usual disclaimer applies.
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Appendices
Appendix A: Proof of Proposition 2
Here, firms compete à la Bertrand in the market. The equilibrium prices, quantities, and profits are,
where \(\overline{a}_{1}=a_{1}\mu \lambda_{0}(1\lambda_{0})[b_{1}c\xi ]\) and \(\overline{a}_{2}=a_{2}\mu \lambda_{0}(1\lambda_{0})[b_{2}\xi c]\).
As before, given the symmetry of the problem, we concentrate on the behavior of firm 1. Firm 1 evaluates the benefits it can get from the agreement and compares the level of profits with and without the agreement. That is, it compares profits in (6) and (16). Participating in an agreement will be profitable if and only if,
After some algebraic computations, the previous inequality reduces to,
Note that (18) differs from (12) in the second term in (round) brackets. Note also that this term is independent of λ _{0}. The second term in round brackets is concave in c, and has one positive and one negative root. Therefore, for positive values of c smaller than the positive root, the term (2b _{1} b _{2}−b _{2} cξ−c ^{2}) is positive, and inequality (18) behaves as (12). Thus, we obtain the same result as in the monopoly case. In contrast, for large enough values of c (beyond the positive root), the term (2b _{1} b _{2}−b _{2} cξ−c ^{2}) is negative, so that the inequality is fulfilled when [1−μλ _{0}(1−λ _{0})]<0 that is, for \(\lambda_{0}\in (\frac{1\overline{\mu }}{2},\frac{1+\overline{\mu }}{2})\), where \(\overline{\mu }= (\frac{\mu 4}{\mu } )^{1/2}\in (0,1)\), for μ>4.
When 0<μ≤4 the first term of (18) in square brackets is always positive. Hence, a solution exists only if the second term in round brackets is positive, i.e. for positive values of c smaller than the positive root.
Appendix B: Proof of Proposition 3
We need to prove that Λ is a Cantor set, namely, that it is a closed, perfect and totally disconnected subset of I. Following Devaney (1985), we structure the proof in three steps.

1.
Λ is a closed set. Let us define G(λ _{0})=1−F(λ _{0}) and rewrite it as G≡1−F. By construction A _{ i } is an open interval centered around 1/2 (see Fig. 4). Let us concentrate on A _{0}. In that case, the function G maps both the intervals I _{0}=[0,λ _{1}] and I _{1}=[λ _{2},1] monotonically onto I. Moreover, G is decreasing on the first interval and increasing on the second. Since G(I _{0})=G(I _{1})=I there is a pair of intervals (one in I _{0} and the other in I _{1}) which are mapped onto A _{0} by G. These intervals define the set A _{1}. Next, let us consider Λ _{1}=I−(A _{0}∪A _{1}). This set consists of four closed intervals (see Fig. 4) and G maps them monotonically onto either I _{0} or I _{1}, but as before, each of the four intervals contains an open subinterval which is mapped by G _{2} onto A _{0}, i.e., the points of this interval escape from I after the third iteration of G. By applying this iterative process, we note that A _{ t } consists of 2^{t} disjoint open intervals and Λ _{ t }=I−(A _{0}∪…∪A _{ t }) consists of 2^{t+1} closed intervals. Hence, Λ is a nested intersection of closed intervals, and is thus a closed set.

2.
Λ is a perfect set. Note that all endpoints of A _{ t }, (t=1,…) are contained in Λ. Such points are eventually mapped to the fixed point of G at 1, and they stay in I under iteration. If a point x∈Λ, were isolated, each nearby point must leave I under iteration, and therefore these points must belong to some A _{ t }. Two possibilities arise. We can think of a sequence of endpoints of A _{ t } converging to x. In this case the endpoints of A _{ t } map to 1 and so, they are in Λ. Alternatively, all points in a deleted area nearby x are mapped out of I by some iteration of G. In this case, we may assume that G _{ τ } maps x to 1 and all the other nearby points are mapped in the positive axis above 1. Then G _{ τ } has a minimum at x, i.e., \(G_{\tau }^{\prime }(x)=0\). This iterative process ensures that it must be so for some t<τ. Hence, G _{ t }(x)=1/2, but then G _{ t+1}(x)∉I and G _{ τ }(x)→−∞, contradicting the fact that G _{ τ }(x)=1.

3.
Λ is a totally disconnected set. Let us focus in the first iteration and assume μ is large enough so that G′(x)>1 for all x∈I _{0}∪I _{1}. For those values of μ, there exists γ>1 such that G′(x)>γ for all x∈Λ. Our iterative process yields \(\vert G_{\tau }^{\prime}(x)\vert >\gamma_{\tau }\). We want to prove that Λ does not contain any interval. Let us proceed by contradiction and assume that there is a closed interval [x,y]∈Λ, x,y∈I _{0}∪I _{1}, x≠y. In this case, \(\vert G_{\tau }^{\prime }(z)\vert>\gamma_{\tau }\), for all z∈[x,y]. Choose τ so that λ _{ τ }y−x>1. Applying the Mean Value Theorem, it follows that G _{ τ }(y)−G _{ τ }(x)≥γ _{ τ }y−x>1, implying that either G _{ τ }(y) or G _{ τ }(x) lies outside of I. This contradicts with our main hypothesis, and thus Λ does not contain intervals. It remains to be determined the μvalues for which the previous argument holds. Finding the values of μ allowing G′(x)>1 means to identify μvalues for which [−μ(1−2x)]^{2}>1. When G=0, this inequality holds for \(\mu >2+\sqrt{5}\). Thus, we have proven that Λ is totally disconnected for \(\mu >2+\sqrt{5}\). Recall from Lemma 1 that we already know that μ>4. Hence, we need to verify whether Λ is also totally disconnected for \(\mu\in (4, 2+\sqrt{5}]\). We appeal to Kraft (1999) who establishes that Λ is a Cantor set for μ>4. The idea behind the proof is that for \(\mu\in (4, 2+\sqrt{5}]\) it turns out that G′(x)⪋1. Kraft argues that the iteration process shrinks some components of I, and stretches some others. His proof thus consists in showing that in the interval \((4, 2+\sqrt{5})\) the stretching is dominated by the shrinking. To this end, he proves that Λ is an hyperbolic set, namely that \(\vert G_{\tau }^{\prime}(x)\vert>k\delta_{\tau }>1\) for x∈Λ, k>0, δ>1.
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MartinezGiralt, X., Nicolini, R. Technological endowments in entrepreneurial partnerships. Comput Math Organ Theory 19, 601–621 (2013). https://doi.org/10.1007/s1058801291448
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DOI: https://doi.org/10.1007/s1058801291448