Keywords

1 Introduction: Toward the Evidence-based Policy Making

The rapid development of cyber-physical systems with the stream of technological change has drastically reshaped the social and economic structures. For this change on the structures, the identification of science, technology, and innovation (STI) policy has become much more challenging than ever before and it is essential to provide more quantitative assessment to reflections on policy engagements to policymakers. For the development of science and technology to cope with societal challenges, it is important that scientists accurately grasp the societal expectation for science, based on observations of the present states of sciences, and show evidence-based alternative policy option to solve the societal problems.

Throughout the transition of economy, the social and economic structural changes have made it necessary to develop policy alternatives to deal with challenges and to conduct prior and ex-post evaluations of policies. At this stage, stakeholders are institutionally required to build policy formation process on a circulated way and advocate the academia to discover societal challenges to identify expectations for science to address challenges on designing policy options and making mutual understanding policy implementation impact assessment and evaluation coping with the challenges. On such evidence-based procession of policymaking, policymakers are suggested to be aquatinted with properties of science in order to deepen the understanding of the properties in the modern sciences and society.

The chapter includes three main sections: In the first section, we introduce the history of science, technology, and innovation development and its implication for the information revolution in the 21st century. In the second section, we specify the concept of R&D capitalized into tangible and intangible asset while using Japan’s input–output (IO) table of 1995–2011 for quantitative assessment of the R&D investment. In the third section, we develop a recursive CGE model, SPAIS-e for STI policy impact evaluation. Finally, we set a scenario and demonstrate the simulation results of the changes of GDP, capital price, and employment of new capital service platform. Policy recommendations for the compilation of IO table for R&D for making evidence-based policy assessment are proposed accordingly.

2 The Advancement of Science and Technology and the Structural Change of Society: The History of Industrial Revolutions

The first industrial revolution started from eleventh to thirteenth century while miller power was widely used as primary energy source. At that time, the cast iron and other highly agriculture mechanism technology had substantially improved productivity. The prosperity has activated the urban commercial life and the curiosity thinking with desire of pursuing knowledge, contributing the establishment of university. The first information revolution was activated by the great discovery period of fourteenth to fifteenth century—the invention of compass, gunpowder, and the spread of letterpress printing has contributed to massive knowledge expansion and resulted in popularity of knowledge for the social material foundation in the fifteenth century.

The second industrial revolution in the sixteenth to eighteenth century was stimulated by steam machine and coal energy. Despite that massive production was made possible through mushroomed factories, creating social status of bourgeois as well as urban slum. The boom of democracy spared from America continent to Europe and that the market mechanism had been emphasized. With the discovery and the use of electricity, the science revolution in the late nineteenth century to early twentieth century that made fundamental change on paradigm was achieved through new quantum physics as the third industrial revolution. Later in the fossil energy era, the massive production and consumption have led to a Trans-Science age with information technology boosted by semiconductor, biotechnology with the second information revolution. The fourth industrial revolution with rapid development of Integrated chip (IC), Internet of things (IoT), Artificial intelligence (AI), life science, and cognitive science has risen substantially in the 21st century.

2.1 Japan’s Development: From High Economic Growth to Stagflation

The experience of Japan’s postwar development is a comprehensive example to witness the entire economic cycle. It could be regarded as one of the successful STI policy instruments that contributed to high economic growth performance during the morden human history. Seventy years after the WWII, Japanese society has experienced a drastic demographic transition from rapid expansion of labor sources due to the demobilization of the soldiers and the repatriates from abroad to aging society with shrinking population since the beginning of the 21st century. After 1945, Japan’s industries had been severely damaged during the war, the rapid recovery because of the aid and abundant labor resource from the rural area to urban area. In this period, industrial reconstruction was made in the designated industries. Since the government was very cautious, the fiscal balance between 1948 and 1965 had been cleared and the deficit has rarely been considered by policymakers. Japan had successful experience of economic expansion between 1960 and 1985 except for the oil shocks in 1973 and 1979. During this period, Japan’s government has been continuing the market intervention through Keynesian fiscal policy by prompting public expenditure on infrastructure and industrial rationalization policies for heavey manufactuing industries. Meanwhile, large science with consumer durable consumption electronic appliances such as refrigerator, washing machine, and television appeared to become common in the household. Flying geese paradigm (Akamatsu 1962) has been proven again in the economic linkage among Japan and other newly industrial economies like South Korea, Singapore, and Taiwan. Vogel’s Japan As Number One (1979) had been one of the best sellers and lessons for successful economy.

In 1985, Japan was forced to adapt the agreement of the Plaza Accord. Afterward, the exchange rate of the Japanese Yen versus US dollar appreciated by 51% from 1985 to 1987. Consequently, Japan suffered from the bubble economy while its economic growth had reached its limitation with the coming of stagflation in the burst of bubble. Since the 1990s, the feature of Japan’s economy had changed from massive consumption market to hobby and high-quality demand along with the expansion of fiscal burden, social insurance, medical care, education expenditure. The damage of Kobe earthquake in 1995 and Asian financial crisis in 1997 had again deterred the confidence, resulting into a more cautious and hesitation for investment and that had frustrated several economic stimulus plans.

Table 1 shows that Japan had positive economic performance till 2000, and on contrary, the stagflation, decrease in population and labor force became severer subsequently. Japan had stepped into hyper-aging society since 2006, with the purpose of leading to sustainable development, and Japan realized that the key solution for aging society, shrinking population, and reduction of fiscal deficit might depend on the knowledge and experiments between science and technology responding to societal problems.

Table 1 Source of average annual economic growth (%)
Full size table

2.2 The Fluctuation of Modernism and STI

From the twentieth to twenty-first century, with the advancement of science and technology, huge issues have been embraced by the entire society. The modernistic social regulations have been undergoing a fluctuation in democracy, market mechanism, and scientific philosophy. Meanwhile, the diminution of energy and resource has been advocating the society to switch from fossil energy to sustainable energy. The globalization led by information technology has somehow inferior the income gap and disparity, while populism has experienced great expansion along with the development of Social Networking System (SNS).

The productivity gain stimulated by aggressive STI spending policy throughout the structural change of society on science and technology. However, the stimulus plans are obligated to follow government’s budgetary concerns with efficiency and feasibility. This may contain the expectation and confidence in the public for STI policy that may satisfy the transparency and the understanding of the public by putting policymaker’s reflections on policy engagements. Traditionally, the policymaking mechanism could be derived into PDCA cycle, referring as “plan-do-check-act” four-step management method used in business for the control and continual improvement of processes and products. Such process has been widely used as a scientific method of implementing STI policy for solving normative approach. The revolutionary development in the information science and technology since the beginning of twentieth century had enormous impacts on all of the science fields including life, material physics, and environmental science methodologically and conceptually. Deepening the properties and the structure of trans-scientific relationships among various sciences is essential to manage the promotion of the STI and apply their results on the policies in order to solve complex problems in modern society.

2.3 The Objective of Science for Science Policy and Policy for Science to Achieve the Co-improvement

STI has been highly expected to cope with societal challenges to appropriately respond to growing economic and social structural changes. Deepening the understanding for the properties in the modern science and society relies on the interpretation of the relation between the technology and modern socio-economy. Moreover, trans-scientific issues which arise in the course of the interaction between science, technology, and society remained unsolved. In order to solve such complicated issues, reliable collaborations among scientists, citizens, and politicians are indispensable in order to fulfill their responsibility.

The development of new trans-sciences has not only brought a variety of benefit, but also unpredictable impacts, disasters, and damages on environment of earth and public controversy. These mean that such impacts could be fairly difficult to understand for their fragile and complex characteristics. A solid and effective collaboration among various science including natural sciences and humanities is strongly recommended with the aim of analyzing their phenomenon scientifically and finds efficient policy instruments as a STI policy.

2.4 Pyramid Hierarchy and the Categories of Industrial Structure

The development of ICT has changed the way of life prevailing from ownership to the right to use. Such transition has made the physical input such as land, building, or merchandise no longer necessary conditions for economic activities. The shared value economy (Fig. 1) may gradually replace the traditional economic mechanism while the material input for production is only secondary and what really matters is the service provided through the newly created platform based on the Internet and SNS that link the demand and supply on their most efficient and least costly pathway. Cases like Uber, Airbnb, YouTube, and other e-commerce have redesigned the nowadays consumption style with sharp advice for the existing IO analysis and its compilation.

Fig. 1
figure 1

Source GRIPS SciREX Center

Transition for consumption and investment.

Full size image

3 Quantitative Modeling for Science and Technology

The impact assessment of STI policy requires the development of adequate modeling frameworks in order to capture the specific characteristics of research and innovation. Their structural equations are estimated econometrically very rich in economic details. Tsujimura et al. (1981) decomposed Japanese economy structure with analyzing economic policy with the interpretation of their general interdependence. Based on the analysis of different periods of Japanese economic development, they presented quantitative theory of price and built a CGE empirical model for quantitative approach. The base of recursive CGE models relies on markets equilibrium balancing supply and demand through the system of prices. Policies that alter the equilibrium are considered shocks that induce new equilibria in the interaction between consumers and producers in the different markets.

There is a general consensus among economists and policymakers that R&D activities play a decisive role in fostering productivity growth. Aulin-Ahmavaara (1999) uses dynamic IO model to examine the effective rates and prices of the inputs treated as produced. The conclusion indicated that such rates are determined by production technology. For recent R&D expenditure studies, Kristkova (2013) indicates that the public R&D sector is not involved in the production of capital varieties, whereas the production of general knowledge is contained in the production processes of both public and private R&D as a specific factor. Comite and Kancs (2015) compares several macroeconomic models, in which they pointed out that modeling public intervention in R&D as a free productive input as determination of optimal policy, or a type of subsidy may be underprovided by the market because of positive spillover across firms. They also suggest that determination of the parameters capturing this effect in the economy should be carefully examined with evidence. The redesign of the STI policy assessment is suggested in order to enable the reconstruction on planning and the implementation of the STI policy, and further, to have such methodology developed as one branch of science. Deepening the understanding of the processes could be operated by involving STI and visualizing their social and economic impacts of STI policy. While the results of these alternative policy options could be examined with scientific evidences, such process is emphasized and regarded effectively with the intention of ensuring transparency in decision-making and provision of accountability to the general public.

3.1 The Capture of Capital Stock Flow

For the assessment of R&D expenditure impact on productivity, we propose a framework of measurement to show the effectiveness of the impacts on the production activities by the accumulation of the knowledge stock through the R&D investment based on Kuroda and Nomura (2004). We try to capitalize the R&D expenditure as an intangible asset from sources of investments for R&D activities provided both from the public and the private by government and private industry. The investments assume to create the knowledge for science and technology, in which the science fields are divided into several fields of sciences. R&D activities are introduced by public and private research institutes like university and affiliated research institutes, independent research institutes as private enterprises, and intra-enterprise R&D activity affiliated by private enterprises. The R&D products and services created by these agencies are accumulated into the knowledge as intangible assets in each agency.

R&D investments are introduced separately investments by government and nonprofit organizations, research institute by private institutions and intra-research activities within the private firm. While introducing the R&D activities into the IO framework explicitly, we could show the theoretical frame to measure how the accumulated intangible assets (knowledge stock) could create the efficiency of the production activities. It is assumed that each R&D investment is accumulated as intangible assets and the capital service flows as technological knowledge are created by the accumulated intangible assets. Capital stock is estimated by perpetual inventory method in tangible and intangible assets by activity in each industry. The real quantity of investment by activity is induced by the nominal investment deflated by price index of investment by activity. The real quantity of investment in time series is utilized to estimate the capital stock by activity in the perpetual inventory method.

Langlois (2002) indicates that if intra-firm R&D activities are assumed to increase the gross output, final demand, and value added shall be explicitly estimated as new concepts. In the current Japanese IO table, intra-firm R&D activity has been taken account of the activity as one independent activity although all of intra-firm R&D activity is aggregated in one activity. The output is transferred into the users as intermediate inputs, but not in the capital formation except capital depreciation allowance. In our analysis, the intra-firm R&D activity could be treated as one of activities in each sector, by which each firm assumed to be able to create new knowledge as one of intangible assets. Furthermore, the capital service flow accumulated in the intangible knowledge stock is assumed to be transferred to the firm main production activity as a type of capital input, but not intermediate input.

3.2 From 1993SNA to 2008SNA

Transaction of knowledge service which is created by knowledge stock is a vital issue. In the 1993 System of National Accounts (SNA), R&D expenditure has been treated as intermediate inputs (United Nations 1993; OECD 2002). Research activities by market producers have been accounted in the gross output in the old SNA in Japan. However, they have not been treated as the transactions in the final demand, but in intermediate transactions. In the revision of JSNA in 2016, the capitalization of the R&D expenditure was regarded in the macroaggregated level as a type of intangible assets. It is treated as one of intangible investment goods, but not as intermediate goods (Cabinet Office of Japan 2017). Since the R&D Expenditure includes total labor cost for R&D, intermediate inputs as well as tangible and intangible capital inputs in public and private research and development activities include university, public, and private research institutes.

The treating the entity of patents and licensing service of patents is also important. Transactions of knowledge service which are creating from knowledge stock by capitalization of R&D activity, most of all, treatments of entity of patents, and transactions of licensing service, should be treated explicitly in SNA and input–output statistics (OECD 2010). The 2008SNA regards entity of patents as fixed productive capital formation; licensing fees of patents as output of the licensing service; and their transactions are treated as intermediate demand or fixed capital formation. However, in Japanese IO table, patent service is not specialized as an industrial sector. Therefore, net transaction of licensing fee of patents are including in the property income in each industrial sector. In the aggregate of the nation-wide, transaction of licensing fee among domestic sectors is canceled out and taken account of the net outside transaction.

The JSNA has revised accordingly with the amendment on R&D from the intermediate inputs to final demand as investment of intangible assets in 2016. The 2015 IO table in Japan will be revised into 2008SNA with inclusion of final demand as capital formation adding to new items of the value added as business surplus and capital depreciation allowance (Kobayashi 2016). On the other hand, intra-firm R&D activities by market producers have not been taken accounts of output measures, but in the business surplus implicitly. Treatment of the capitalization of R&D expenditure based on SNA2008 has been employed in many countries such as Australia (2009), Canada (2012), USA (2013), Korea (2014), and UK (2014), respectively.

3.3 Data Structure

For the sake of making coevolutionary relationship among scientists, citizens, and politicians, and activating STI capabilities for value capture in the society, we aim to construct a policy simulator to give evidence-based policy options. We compose the following data framework for our objectives. In the measurement of private or public R&D expenditure, such investment activity in capital stock should be regarded from dimensions of tangible and intangible knowledge capital stock. As suggested by 2008SNA, the R&D investment should be taken as intangible knowledge stock formation. Such compilation has been done in Japan’s 2016 national accounts as an aggregated measure but not yet the IO table of 2011 (released in 2015). The data used in our model (Fig. 2) were sourced from Japan’s IO tables and with extended estimation (red blocks) to distinguish tangible and intangible capital investments by 95 sectors (Table 3), considering long-/short-run block, labor market modeling, value added and wage determinant, government balance sheet, and the final demand block.

Fig. 2
figure 2

Source Kuroda et al. (2016)

Structure of input–output data layers.

Full size image

In advance, here we try to revise the Japanese IO Tables to be capitalized the R&D investment during the year 1995–2011. The production activity at the year t depends upon the tangible and intangible capital assets accumulated at the prior years and embodied the technological properties at the time when they were invested. Through investment, accumulated productive capital is composed of the capital goods from prior periods. The idea could evaluate the contribution of knowledge stock which is accumulated by the R&D investment as intangible assets. We assume that the knowledge of science and technology is accumulated and deepened by the R&D activities with R&D investment. Then we assume that the R&D investment is accumulated as intangible assets and intangible asset creates knowledge proportionally to the amount of intangible capital stock.

In our IO table, R&D activities are assumed to be separately identified as intra-firm R&D activity, independent public and nonprofit private R&D activities including government institutes and nonprofit institutions and independent private R&D industrial activity. In each industrial sector excluding the above R&D activities with the assumption to be divided into the production activity of main products and infra-industry R&D activity. We also assume that independent public and nonprofit private R&D activity and private R&D industrial activity are divided into several fields by science and technology.

Figure 3 shows that sectors except research institutes by government and industry are divided into the following three categories: (a) main product; (b) intra-firm ICT activity; and (c) intra-firm R&D activity. Under such disaggregation, we may obtain a systematic view of capital service flow facilitated by the ICT/IoT implementation for information allocation and processioning to accelerate its productivity for manufacturing. The R&D activities by industry and government as independent research institute are divided into ten research fields of ICT, environment, materials, energy, space, oceanography, other natural sciences and social sciences and humanity.

Fig. 3
figure 3

Source GRIPS SciREX Center

Data framework—production activity by sector.

Full size image

In order to analyze the impacts of the development of the knowledge in science and technology on the economy and society, it is necessary to establish the analytical tool to observe the impacts theoretically and empirically. Capital stock matrix both in tangible and intangible assets assumes to be estimated by the perpetual inventory method with given economic rate of replacement. We can define capital coefficients by tangible and intangible assets as the ratio by capital stock and output in main products and R&D activities, respectively.

The intra-R&D activity and intra-information activity including software development in-house from the main productive activity in the enterprise are classified separately. The knowledge services created inside of the enterprise are counted as capital formation of intangible assets and accumulated to the intangible assets. On the other hand, the fixed capitals which are used in the intra-activity of R&D and software development are counted at the fixed capital formation as investment and they are accumulated to the tangible assets in these two activities. In our model, these factor inputs represent the capitalization of intangible assets so that such capital inputs are measured by quantity and price of the capital services which were imputed from capital stock and capital cost. The capital stocks and capital cost are measured consistently with IO tables as for tangible and intangible assets while the intangible assets are separately estimated by software and knowledge stock in research fields. Finally, the capital formation matrices by flow and stock for tangible and intangible assets were estimated annually while labor inputs by sector and activity are separately estimated.

4 The Mechanism of Policy Formation and Its Evaluation

For activating science and technology capabilities for value capture in the society, we constructed a recursive CGE model (Kuroda et al. 2016) that illustrates the new business platforms reflecting the investment on R&D for facilitating capital service flow (Fig. 4). The model is expected to shed lights on implication of total factor productivity (TFP) for its process change on the demand side while the productivity improvement in information provision service sector that enlarges the platform business, assisting manufacturing sectors to create new market and variate the international production networking structure.

Fig. 4
figure 4

Source GRIPS SciREX Center

Recursive CGE model structure.

Full size image

On such platform, scientists need to move from Science for Science’s sake to Science for Society while policymakers want to design an evidence-based STI policy scientifically to realize the capability of science and technology toward the value captured, while the predetermined endogenous variable such as capital stock and wage interacted with exogenous variable of technology and population generates the output of short-run equilibrium and determines new equilibrium as the predetermined variables for the next time period.

Moreover, the information management could benefit from outsourcing and externalization while the cross-sectional platform of information management may thus be established. The simulation results showed the change on employment and production division along with the ICT/IoT advancement of its short-/long-run effect.

4.1 The SciREX Policy Intelligence Assistance System—Economic Simulator (SPIAS-e)

In this process, the change of capital service and labor service could be observed, indicating the gap of income and capital formation. There are different level of procession/production efficiency and are set technology parameters in the activity divisions of marketing, planning, R&D, procurement, operation and sales, maintenance will be calibrated through a database system—SciREX Policy Assistance Intelligence System (SPIAS) containing research grants, academic performance based on scientific papers, patents, and news releases.

Based on the structure of the recursive CGE model, we develop a user-friendly simulator “SPIAS-e” affiliated in our SPIAS platform. The key parameters and the volume of government R&D investment could be easily controlled with the visualized results of the year 2005–2050 comparing the business as usual (BAU) path on GDP growth, impact, changes on indicators and stock in visualized graphs of policy options (Appendixes).

4.2 Scenarios

Two scenarios are made to examine the impact of R&D investment on medical service sector (Table 2). The BAU scenario gives us the overview of the baseline economic and social trend until 2050 from the year 2005. The R&D investment (tangible and intangible) made by government remained the same and no improvement of technology production while the structural changes of the population by age and gender are assumed exogenously. The ICT+R&D scenario referred to more R&D investment in ICT and other science R&D fields, with higher efficiency to be actualized from the year 2020 in production efficiency led by more R&D investment in the designated sectors such as semiconductor, software, Internet, information management, and communication service-related sectors. The knowledge stock accumulated by the government R&D expenditure is assumed to have an impact on the productivity increases in the private sectors as public goods thanks to the patent released.

Table 2 Scenarios and parameter setting
Full size table

4.3 Assumptions on Exogenous Policy Variables for the Baseline Scenario

We have set assumptions for Baseline scenario of the Japanese economy during the future years 2020–2050 as following: (i) Government R&D expenditure scale remains the same during 2005–2050; (ii) Constant tax rates including personal income tax, corporate income tax, consumption tax, indirect tax and property for the 2005 level; (iii) Government consumption expenditure will be assumed to be constant to nominal GDP endogenously; (iv) Government capital formation for tangible and intangible assets will be fixed at the 2005 level nominally; and (v) Structure of the population will be assumed to be given by the projections with fertility medium-variant case by National Institute of Population and Social Security Research.

4.4 Simulation Results

When R&D investment policy options of government R&D expenditure and assumed productivity efficiency improvement are inserted in the SPIAS-e, the system on Webpage generates the projection of economic performance and indicators 2005–2050 (while the results from 2005 to 2011 are the actual data) as alternative policy option assessment. The GDP growth (upper panel) and percentage change (middle panel) and the breakdown of GDP growth contributed by value add (lower panel) are displayed in Fig. 5. (Abbreviations could be referred to Table 4.)

Fig. 5
figure 5

Source GRIPS SciREX Center SPIAS-e

GDP change and contribution by value add (unit: million JPY, %).

Full size image

Simulation results showed that ICT+R&D scenario will lead to higher real GDP since 2020 and gradually increase its growth path. The GDP change seemed to fluctuate from the BAU but remained on positive scale. While the production efficiency improvement was actualized in 2020, the GDP growth reached its peak by 0.8% and decrease due to its marginal effect diminishing. In the breakdown of the contribution, it could be found that the share of OS (operation surplus) and DEPK (tangible capital depreciation provision) kept increasing and served as the growth engine with its spillover pull for the economy.

4.5 Employment Change

The employment by gender and age has also been an important concern as the economic indicator and assessment. The employment projection on three representing sectors of selected capital service platforms is (Fig. 6): Information and Communication, Software and Information Management Service.

Fig. 6
figure 6

Source GRIPS SciREX Center SPIAS-e

Sectoral employment change (unit: person).

Full size image

Overall speaking, the aging society and shrinking population seem to be inevitable while the employment in the three sectors showed a continuous drop. In the group of younger age 15–34, the trend has been decreasing mainly due to the shrinking population; whereas in categories of age over 65-year old, the share of employment continue to growth, implying that the ICT and R&D investment could stimulate the employment for the whole generation and sustain the employment till their silver age. The information management service overtook the information and communication sectors in a longer term.

Comparing the capital service price among six sectors that highly related to the ICT/IoT (Fig. 7), sectors such as Software and Information and Communication showed a vibrant growth while computer and semiconductor revealed decrease, most of all in Internet sector. On other hand, commerce remained steady with only very slight improvement. This comparison showed that the new platform created in the new society will be mainly led by information revolution while software and information management service will have most significant role. These newly boomed sectors may overtake the traditional commercial mode; nevertheless, they may well face drastic business cycle, which actually reflecting the current situation of SNS platform. In spite of its essential role supporting the new platform base, the Internet sector will just become a basic provision and thus no more additional surplus granted.

Fig. 7
figure 7

Source GRIPS SciREX Center SPIAS-e

Capital service price index.

Full size image

4.6 Disguised Unemployment and Work Sharing

Under the current model structural and predetermined inter-temporal formula, with the assumption of fully employment. The declining labor supply, especially in working hour may imply that much labor-intensive chores, could be done by senior citizen with the support of ICT and robot. The fact of less working hour and the substitute of man labor by ICT and event robot for “working sharing,” creating another phenomenon of “disguised unemployment.”

By comparing the divisions of employment multiplying real hourly wage between BAU and policy option, Fig. 8 shows the trend of disguised unemployment in the three categories. The declining number of intra-ICT indicated the massive demand of employment in such sector, while main product and intra-R&D only decrease in a mild pace.

Fig. 8
figure 8

Source GRIPS SciREX Center SPIAS-e

Disguised unemployment in three categories (unit: thousand people).

Full size image

Up to date in 2018, a decent job regarded a reasonable work is 8 h a day (40 h weekly or 1775 h annually). With higher production efficiency led by the technology advancement, declining working hour is foreseeable in the very near future, just as the implementation of two-day weekend and holiday system in the twentieth century. The simulation results provided the motivation by social needs and through innovation that provides feedback to current social economy and leads new social economy. With less working and more leisure time, leaving human being more freedom to consider complicated issues for achieving a sustainable socio-economy under more newly established service platforms. The improvement of work and life balance could contribute to quality of life (QoL) improvement.

5 Concluding Remarks

The impact assessment and evaluation of STI policy options have been regarded top priority for Japanese government to allocate its budget efficiently and effectively. The simulation results calculated by SPIAS-e illustrate a possible picture of Japan’s development under a society of aging and shrinking population. It is implied that various system and reforms should be established to cope with the change of social structure as well as the new service platforms. The SPIAS-e is not only an evidence-based, user-friendly tool but mostly it demonstrates a platform to understand the process and socioeconomic change among “good translational relationship” between natural and social and humanity sciences.

Based on the 2008SNA, the new IO table compilation of R&D will help to make more accurate STI policy recommendations. With the development of the system and more precise calibrations on the technologies, the quantified and visualized results could assist policymaking. An important and necessary condition is to create “policy options” with consistency and accountability to redesign the new social and economic structures. SPIAS-e is expected to help policymakers better understand the substitution between machine and unskilled labor, and the expansion of income differences domestically and internationally.

While the recent rapid improvement of AI and Big-Data is contributing to the effective collection of data that will help us redefining neoclassical economics for obtaining sustainable development. Confronting the challenges of aging society with shrieking population, the lack of effective demand should be solved by continuously created platform of knowledge-based open innovation so that such unstable conflicts among countries with perception gaps could be observed and overcome.