Technology lock-in with horizontal and vertical innovations through limited R&D spending

In this paper we analyze an inter-temporal optimization problem of a representative firm that invests in horizontal and vertical innovations and that faces a constraint with respect to total R&D spending. We find that there can exist two different steady-states of the economy when the amount of research spending falls short of an endogenously determined threshold: one with higher productivities and less new technologies being developed, and the other with more technologies being created and lower productivities. But, for a higher amount of R&D spending the steady-state becomes unique and the firm produces the whole spectrum of available technologies. Thus, a lock-in effect may arise that, however, can be overcome by raising R&D spending sufficiently.     

On the prisoner’s dilemma in R&D with input spillovers and incentives for R&D cooperation

This paper considers a standard model of strategic R&D with spillovers in R&D inputs, and extends the result that duopoly firms engaged in a standard two-stage game of R&D and Cournot competition end up in a prisoner’s dilemma situation for their R&D decisions, whenever spillover effects and R&D costs are relatively low. In terms of social welfare, this prisoner’s dilemma always works to the advantage of both consumers and society. This result allows a novel and enlightening perspective on some issues of substantial interest in the innovation literature. In particular, the incentive firms face towards R&D cooperation in the form of an R&D cartel is shown to be maximal for the case of zero spillovers, which is when the prisoner’s dilemma has the largest scope.     

Real R&D options and optimal activation of two-dimensional random controls

We value investments under uncertainty with embedded optional costly controls (impulse-type with uncertain outcome) that capture managerial intervention for value enhancement and/or information acquisition (exploration, R&D, advertising, marketing research, etc). Implementing real option models but neglecting such embedded managerial actions can severely underestimate investment opportunities and lead to erroneous investment decisions. Optimal decisions are solutions to a maximization problem where the trade-off between the control's cost and the value added by such actions is explicitly taken into consideration. In this paper, we generalize such a methodology from one dealing with the special case of actions affecting only one state-variable, to one with actions that affect several. Asset values follow geometric Brownian motion or jump-diffusion processes with multiple generating sources of jumps. The Markov-chain numerical methodology we provide can handle sequential controls. Although we report the results with open-loop policies, the approach can be readily extended to accommodate dependency among the controls.     

A Multi-criteria Decision Support System for R&D Project Selection

This paper describes the development of a decision support system for the selection of a portfolio of R&D projects, which was carried out for a large electricity utility corporation. The DSS was constructed round a reference point approach for the underlying multi-criteria decision problem. The application of this approach did require a less usual form of scalarizing function as well as a heuristic algorithm for solving a non-linear knapsack problem. Practical aspects of the implementation of the multi-criteria approach in a DSS operating on a PC are also discussed.     

A comparison between the robust risk-aware and risk-seeking managers in R&D portfolio management

In this paper, we analyze two mathematical modeling frameworks that reflect different managerial attitudes toward upside risk in the context of R&D portfolio selection. The manager seeks to allocate a development budget between low-risk, low-reward projects, called incremental projects, and high-risk, high-reward projects, called innovational projects. Because of their highly uncertain nature and significant probability of failure, the expected value of the innovational projects is smaller than that of their incremental projects’ counterpart, but the long-term financial health of a company necessitates to take risk in order to maintain growth. We study the differences in strategy and portfolio’s risk profile that arise between a risk-aware manager, who takes upside risk because he has to for the long-term competitive advantage of his company, and a risk-seeking manager, who will take as big a bet as allowed by the model. To the best of our knowledge, this is the first paper to consider upside risk management using a robust-optimization-like methodology.     

R&D Project Selection in a Multidimensional Environment: A Practical Approach

This paper investigates the R&D project selection problem within government departments. The Department of National Defence is taken as a case in point. The multidimensional character of the problem is detailed, and existing methods for priority allocation are briefly surveyed. Two basic models for selecting projects are described. One of these, the ordinal intersection method, was found to be the most appropriate for the actual problem settings investigated. An illustrative example is given, and concluding remarks are made.     

The Assessment of Reservation Prices of an Intermediate R&D Result and Patent Reward Under Oligopolistic Competitive Behaviour

One of the shortcomings of R&D evaluation is a lack of emphasis on analytical assessment of the value of an on-going R&D project. This paper addresses the problem concerning the distributive aspect of access to superior knowledge. Decision tree analysis and probability models appear to be appropriate tools for assessing the values of an intermediate result and patent reward of a firm's R&D decision in an environment of perfect information and oligopolistic competition. The assessed values are used as minimum prices acceptable to the firm when the knowledge is disseminated to the public. This paper will attempt to resolve this problem through the determination of the appropriate values of the reservation price of the first-stage invention in terms of the final reward and of a patent reward for which the inventor is willing to apply.     

Planning and Control of R & D Activities

Despite the success of network techniques for planning maintenance and construction projects there has been little application in R & D. Extensions to network planning techniques to allow their use in R & D are described and potential areas of cost and benefit stemming from their use identified. Some applications of planning methods in R & D are considered and conclusions are drawn concerning the difficulties to be faced, and the strategies to be adopted, in their implementation.     

Probabilistic Networks and R & D Portfolio Selection

Mathematical programming methods have been suggested and used as an aid to R & D project portfolio selection. One of the main criticisms of the use of such models is that the stochastic nature of the problem has been largely ignored. This paper presents a method which takes into account the stochastic nature of resource requirements and project benefits, using a combination of probabilistic networks, simulation and mathematical programming. A case study based on data from an industrial R & D laboratory is presented and compared with the use of expected value methods. The results of the study indicate that in this particular case the deterministic linear programming solution is robust.     

Selecting a portfolio of nonhomogeneous R&D proposals

This article reports a procedure developed to assist the U.S. Department of Energy in the selection of a portfolio of proposals addressing the design and manufacture of components of solar photovoltaic arrays, and complete photovoltaic systems. Considerations such as the relative importance of the different components in the overall effort, compatibility of the proposed designs, and budgetary and programmatic issues were complicating factors. The technical quality of each proposal was represented by multiple evaluation measures. These were combined into a single index through the use of multi attributeutility function. Indices representing the relative importance of the various components were then developed, and results in measurable value theory were applied to derive an index of technical quality of a portfolio of proposals. The problem was then formulated as an integer program. It was solved by using an interactive branch and bound code that allowed for sensitivity analysis before selecting the portfolio of proposals for funding.The procedure was successfully applied to funding a portfolio of proposals aimed at reducing the cost of producing photovoltaic arrays, and to several other government-sponsored procurements.     

Evaluating pharmaceutical R&D under technical and economic uncertainty

This study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic uncertainty is modeled as a standard diffusion process which incorporates both up-and downward shocks. Practical application of this method is emphasized through a case analysis. We show that both uncertainties have a positive impact on the R&D option value. Moreover, from the sensitivity analysis, we find that the sensitivity of the option with respect to economic uncertainty and market introduction cost decreases when technical uncertainty increases.     

Effects of heat transfer and space porosity on peristaltic flow in a vertical asymmetric channel

This article discusses the effect of heat transfer on the peristaltic flow of a Newtonian fluid through a porous space in a vertical asymmetric channel. Long wavelength approximation is used to linearize the governing equations. The system of the governing nonlinear PDE is solved by using the perturbation method. The solutions are obtained for the velocity and the temperature fields. The flow is investigated in a wave frame of reference moving with velocity of the wave. Numerical calculations are carried out for the pressure rise, frictional forces, and the features of the flow and temperature characteristics are analyzed by plotting graphs and discussed in detail. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2010     

Climate change and optimal energy technology R&D policy

Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.     

A model of R&D valuation and the design of research incentives

We develop a real options model of R&D valuation that takes into account the uncertainty in the quality (or efficacy) of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following five dimensions: expected cost to the sponsor, probability of development success, consumer surplus generated, expected number of successful vaccinations and expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies). Moreover, we find that a hybrid subsidy plan constructed from a purchase commitment combined with a sponsor research cost-sharing subsidy is the most effective.     

A model of R&D valuation and the design of research incentives

We develop a real options model of R&D valuation that takes into account the uncertainty in the quality (or efficacy) of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following five dimensions: expected cost to the sponsor, probability of development success, consumer surplus generated, expected number of successful vaccinations and expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies). Moreover, we find that a hybrid subsidy plan constructed from a purchase commitment combined with a sponsor research cost-sharing subsidy is the most effective.     

面向产品研发任务的知识层次协调模型及仿真研究

从微观视角量化分析产品研发团队中的知识协调过程,可以更为深入地揭示本质性规律.在理论研究基础上,提炼产品研发任务的复杂性,并提出团队学习是实现知识协调的重要方式.将研发任务的完成以相应知识层次的概率密度函数予以表征,并将团队中的个体学习与协同学习表述为对应层次的知识水平增长.从而构建出复杂知识性任务情境下的知识层次协调...     

性别差异对研发投资决策影响的实验研究

基于Z-Tree实验平台利用研发投资自然博弈实验研究发现风险规避导致女性的研发投入强度低于男性,但女性在研发投资过程中愿意投入的研发努力程度高于男性。随着时间推移,男性的研发努力程度明显下降,但女性的研发努力程度反而上升。男性和女性在决策中均表现出心理账户效应,女性更加愿意通过努力来提高业绩。男性在投资中更多体现期权思想,而女性更多表现出长期倾向下的持续努力。文章在揭示性别因素影响研发投资机理基础上,为董事会性别多样性、分级董事会以及递延薪酬等治理机制发挥作用机理提供了新解释。研究结论可以为企业职位的性别配置、分级董事会以及激励契约设计提供借鉴。     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.