# Presentation: 273877

## Science of Science and Innovation Policy(SciSIP)

- Presentation to:
***SBE Advisory Committee***
- By:
***Dr. Kaye Husbands Fealing***
- National Science Foundation
- November 8, 2007

**Notes:**
Basic science v. policy—we are on the basic science side, with broader implications for industry, government and the academy—the “triple helix.”

Both scientific discovery and innovation are part of our initiative.  These sectors are cooperative and inextricably linked. 

Definition of innovation--product, process and organizational.

Old joke that economists tell about a man looking for his keys under a lamppost.  When asked where he lost the keys he says in the ally but there is no light there.  

Clearly we need to establish better understanding and metrics of the global innovation system.  We need to populate more areas of observations and analysis to inform our science and innovation funding, management and protection decisions or mechanisms.

## Dr. Marburger’s Priority Areas

- Comprehensive **data** collection, data taxonomy and stewardship
- New **metrics**, **models**, **tools**, and **frameworks**
**International partnerships** that promote science and technology advancements

**Notes:**
AAAS April 2005 and Blue Sky II (Ottawa) September 2006—science of science policy, particularly focused on the global ecology of innovation.
(American Association for the Advancement of Science)
Three legs—data, models and tools—leads to comprehensive framework on science, technology and innovation
International scope, particularly since knowledge generation and implementation often times knows no geographic boundaries.  
While funding could be determined at the company, local, state, or federal level, the transfer of new knowledge into the marketplace transcends these artificial boundaries.
Past investments in basic scientific research have had an enormous impact on innovation, economic growth and societal well-being.  
However, there is modest capability of predicting how best to make and manage future investments that yield the most promising and important opportunities. 
Global science and policy communities must, therefore, collaborate on advancing a new interdisciplinary field of science policy studies.
We need a thorough mapping of the ecology of innovation.

## SciSIP GoalsDevelopment of an evidence-based platformfor science policy

**Understanding**:  develop usable *knowledge* and *theories*
**Measurement**:  improve and expand science* metrics*, *datasets* and *analytical models and tools*
**Community development**:  cultivate a *community of practice* focusing on SciSIP across the academy, the public sector and industry

**Notes:**
SBE’s SciSIP initiative is developing the foundations of an evidence-based platform from which policymakers and researchers may assess the impacts and improve their understanding of the dynamics of the nation’s scientific and engineering enterprise.  
Don’t fool ourselves, however, we am talking about a portfolio of choices and strategies.  
NO MAGIC BULLET.

## Community of Practice

**Interdisciplinary**
**International**
**Innovative**
**Inclusive**

## Grand Challenges (1)

**Full systems approach** to mapping science, technology and innovation
**Portfolio models** of investment in science and technology
**Behavioral and dynamic models** of the relationship between scientific discovery and policy decisions
**Mapping and cyber tools **linking the evolving taxonomy of S&E to policy decision-making
- Full accounting of **intangible assets and international workforce flows**, and their contributions to science and technology outcomes

**Notes:**
(1) Call for new data and research on institutions

(3) Well being—govt (defense); private sector (agriculture & energy); public good (health, environment, security, discovery)

## Grand Challenges (2)

- Real-time evaluative and decision-making tools for assessing public sector investments in fundamental science and technology on economic growth and** social well-being**
- Measures of **spillover effects** between scientific discovery and technological innovation, particularly among universities, firms and government labs
- Evaluative measures of disciplinary cultures on **transformative work**
- Computational models of **creativity**
- Evaluative approaches to measuring **diversity** and its impact on science and technology developments

**Notes:**
(6) Tools beyond growth

(7) Understanding knowledge transfer

***Identify net exporters and importers of knowledge by fields/disciplines

## SciSIP-Funded Investigator Initiated Research (1)

**Human capital development and the collaborative enterprise related to STI outcomes:**
- Trandisciplinary research teams (*NIH-CERN; biomedical; nano*)
- Collaboration between academic and non-academic scientists (*hydrology, soil and water science*)
- Virtual social networks (*Internet*)
- Domain and culturally based evaluation tools (*U.S., Mexico, Brazil*)
**Returns to international knowledge flows:**
- Benefits from international collaboration (*bio-fuels*)
- Contributions of foreign graduate students and postdocs to knowledge creation and diffusion
**Creativity and innovation:**
- Cognitive models of scientific discovery and innovation
- Tools for innovative design based on core cognitive processes

**Notes:**
Theoretical and conceptual models of scientific discovery and technological innovation
Statistical and econometric models and tools for estimating returns to science and engineering investments
Qualitative tools
Scientometric and cyber tools
Many levels of analysis:  cognitive, organizational, cultural, regional, national and transnational
Interdisciplinary and international collaborations
“In recent years there has been a new focus on 'creative industries' as a source of market generated wealth. It has beenargued that activities which stimulate people's creativity, imagination and critical thinking...can release vast and thusfar untapped potential for economic innovation, enterprise and entrepreneurship, to the benefit of efficiency, competitiveness andeconomic development more generally.” Guest Editors: Silvia Sacchetti, Roger Sugden, Guest Editors, International Review of Applied Economics

## SciSIP-FundedInvestigator Initiated Research (2)

**Knowledge production system:**
- Gap analysis of the Idea Innovation Network (*normal v. high risk; small v. large science*)
- Complexity systems modeling of technological evolution (*low-carbon energy technologies*)
- Mapping tool of science for correlating funding with research outputs
- International database of inter-organizational collaborative agreements (*OECD*)
**Science policy implications:**
- Theoretical framework for assessing science and technology policies and social welfare outcomes (*OECD*)
- Evaluative tools for assessing the distributional consequences of policy initiatives (*intellectual property rights; life sciences*)
- State science and innovation policy initiatives evaluation tools
- Public-values-based model of science and innovation policies

## Broader Impacts

- Simulation models of the knowledge creation and transfer system
- Organizational designs and social networks that incubate, enrich and accelerate innovation
- Tools for policymakers to optimize funding potential
- Database of international research and technology partnerships, with indicators
- Video database on tools and artifacts in innovative design
- Performance evaluations tools enabled by cyberinfrastructure
- Frontier methods of program evaluation
- Theoretical foundations of the knowledge creation and innovation system and linkages to economic growth and social well-being

## Lessons Learned from Solicitation I

- Proposals from a variety of fields and varied methodologies performed well in the competition.
- Multidisciplinary studies performed well ***if*** all of the necessary areas of expertise were well represented by engaged researchers.
- Requests for small grants to do pilot studies were underutilized.
- Research studies outperformed infrastructural development.
- Research agenda should focus on Grand Challenges—development, organization and mobilization of productive resources in the creation and diffusion of new knowledge.
- Data development and community building are two important emphasis areas to be added next competition.

## SciSIP Milestones

**Near term:**
- SciSIP Solicitation II
*Add* new methods, models and tools specifically informing the data-collection process
*Add* data development including new surveys, datasets, indicators, and benchmarks
- Stakeholder forum—academics, policymakers, industry tech managers
- Collaboratories—virtual organizations
**Medium term:  **
- New S&E indicators
- Domain-specific models
- Summer institute
**Longer term:**
- An evidence-based understanding of the impacts of the S&E enterprise
- A capacity to better nourish and harness the capabilities of the national STEM workforce
- The development of a Community of Practice

**Notes:**
New data—next big push.
Will have Solicitation II that is due out later this fall.
Summative conference—triple helix—academics, industry technology managers, policymakers

## Thank you!

- Comments and questions invited.
- For more information please contact:
- Dr. Kaye Husbands Fealing
- khusband@nsf.gov