Throughout 1980's, Japanese science and technology policy was focused on promoting the basic research in the public sector, and encouraging the private sector to go into more basic fields instead of development of improved products. In fact, the annual expenditure for the basic research increased by 50 per cent during ten years since 1988. However, after the end of the "bubble economy", the outcome from the investment in the basic research has been seriously questioned. While the shares of Japanese papers and their citations in the scientific journals have increased by about 2 points, contribution to creation of new technologies fell short o expectation. The present paper discusses reasons for this situation and points out strategic inconsistencies in both the government and industry. Strengthening the competitiveness of Japan in the coming decade necessitates in a new mechanism to develop appropriate strategies for promising results already accumulated.

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Recent interest in artificial intelligence (AI) has necessarily led to is applications in financial engineering. Possible applications include: (1) "technology" such as risk management, price theory and performance evaluation, and (2) "art" such as prediction profitability and risk in future (usually the standard deviation of profitability). While the "technology" may advance relatively easily by application of theories of probability and statistics, the "art" depends much on investor's philosophy, which makes any "standard model" impossible. This is the very area where AI is beginning to find applications. After presenting a review of financial engineering, the present paper presents two examples of AI application in this field: a simulation of long-term forecasts of interest rates, and a virtual market studies in the Santa Fe Institute. In addition, methods and effectiveness of AI in financial engineering are discussed in comparison with applications in other fields.

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The validity of the Leland model, recently developed in the U.S., in the Japanese market is discussed by examining the insolvency risk premium in capital, shareholder capital and debt costs, with taxes neglected. The Leland model permits calculation of the company, shareholder and debt values with insolvency taken into account. Use of these values in calculation of the insolvency premium requires formulation of capital cost-company value, shareholder capital cost-shareholder value, and debt cost-debt value relationships. Assuming that the stochastic change of company value results from that of cash flow, the present study uses the cash discount model for these formulations, which are then used for calculation of the discount rate corresponding to company value, or the capital cost. Comparison of capital costs with and without insolvency gives the insolvency risk premium in capital cost. Similar calculations are made for shareholder capital and debt costs. Calculations based on current data on the Japanese financial market show that the insolvency risk premium increases rapidly when the debt ratio (i.e. the debt/company value ratio in the current values) exceeds 70%: when the debt ratio is 80%, the shareholder capital cost is about 20%, in which about 10% is the insolvency premium risk. These results are considered well representative of the present circumstances.

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History of development of financial engineering in the U.S. shows that many results were obtained in studies with actual business activities as the background, which have, in turn, readily found practical applications. In Japan, however, factual analysis has been hindered by various problems in both academic and business circles. Given that the social needs for financial engineering is certain to grow, the current situation should be improved to assure fruitful applications, with the limitation inherent to research in this field taken into account.

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In the "first financial engineering boom" in the latter half of the 1980's, Japanese financial organizations tried to introduce advanced financial technologies developed in Europe and the U.S., and recruited many science/engineering students. Although this boom was short-lived and vanished with the end of the "bubble economy", research efforts in this field were maintained until the advent of the "Japanese financial big bang", or attempts at radical reform of the financial system of the country, including opening up the market to other countries. This triggered a second boom, in which science/engineering schools, traditionally aloof from finance, began joining field. Following a brief summary of this development and the present states of financial engineering, the author presents a proposal on future curriculum in this field.

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各産業の研究開発活動及び事業活動における多角化度を定量的に分析した。多角化度については、エントロピー値という指標により測定した。1980年代においては、多くの産業で研究開発活動の多角化が進展しており、事業活動の多角化についても、研究開発活動に追随するように進展している。次に、研究開発活動及び事業活動の多角化データと産業連関表の産出投入表のデータを用いて、多角化の方向性を川上多角化度及び川下多角化度といった指標により定量化した。そして、研究開発の多角化の方向性を各産業の技術機会と捉え、産業カテゴリー毎に技術機会の方向性を整理した。分析の結果、ハイテク産業及び規模依存型産業は、川下方向への技術機会があり、科学技術と関連のある繊維・医薬・紙パ産業では川上方向への技術機会があると考えられる。さらに、本研究では、収益性向上に資する多角化戦略を検討するため、事例分析を行い、付加価値の高い川下方向への技術の展開を図る多角化戦略が収益性向上に寄与するという仮説を導き出した。最後に、この仮説を示唆する実証結果を多変量解析により得た。

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今日、我が国では大学による新産業創造への貢献が求められ、各種の施策が進められようとしているが、産学連携が本格的に進展している状況にはない。一方、米国では大学において様々な形態での産学連携が進展し、新産業の芽となる技術の創出およびその移転が活発に行われ、好調な米国経済を支える一要因となっていると言われている。 本稿では、大学からの産業創造が活発に行われている米国の研究大学(MIT、スタンフォード大学、カリフォルニア大学バークレー校)と日本の研究大学(東工大)の産学連携システムおよび産学連携の進展状況の比較検討をするとともに、産学連携進展のモデルを提示し、産学連携のメカニズムの分析・ケーススタディを行った。更にこれらの分析を踏まえ、今後の日本の大学における新産業創造による社会貢献を進めていくための検討課題等について考察した。

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