Strategic Management of R&D Capabilities with Agent Based Modeling
R&D efforts produce more than just the tangible prototype or patent. These efforts also create intangible attributes that are manifested as a capability, delivering an organization the endogenous skills needed to traverse a technological landscape in a more efficient and successful manner than t...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/147459 |
| _version_ | 1826204611294265344 |
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| author | Paul, Jason V. |
| author2 | Moser, Bryan R. |
| author_facet | Moser, Bryan R. Paul, Jason V. |
| author_sort | Paul, Jason V. |
| collection | MIT |
| description | R&D efforts produce more than just the tangible prototype or patent. These efforts also create intangible attributes that are manifested as a capability, delivering an organization the endogenous skills needed to traverse a technological landscape in a more efficient and successful manner than the competition. The challenge for leadership is aligning resources and policy to balance the tangible and intangible to meet organizational strategy. This is challenging since the intangible outputs are not easily quantifiable. However, Agent Based Modeling (ABM) provides one method to simulate R&D activities to quantify and compare the relative gains between the tangible and intangible outputs. This type of modeling incorporates agents that move about an environment while making decisions based on their interactions with the environment and other agents. Due to the stochastic foundation of this model, a Monte Carlo approach is used and the results are shown as a cumulative distribution function that could allow leadership to compare the relative impacts of different R&D strategies or policies. This work presents a model with agents in the form of researchers that are pursuing an innovation goal. They traverse a technology landscape, ultimately creating a path to realize the innovation goal. The landscape is littered with technical impediments and potential serendipitous discoveries. The researchers must overcome these barriers through individual or collaborative research efforts or avoidance. This model is exercised for self consistency and cross consistency through 3 scenarios to increase confidence. The model is then expanded to include technology areas of lower maturity with higher densities of technical barriers and serendipitous discovery sites and a scenario where researchers conducting basic research work in conjunction with researchers conducting applied research. In these last 2 scenarios, the data highlight the tradespace in a realistic scenario, giving leadership the data to determine best resourcing decisions to achieve organizational strategic goals. Specifically, in both of these scenarios, the time required to achieve the primary innovation goal is greater than the base case but shows a marked increase in organizational knowledge gains and serendipitous discoveries. |
| first_indexed | 2024-09-23T12:58:11Z |
| format | Thesis |
| id | mit-1721.1/147459 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:58:11Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1474592023-01-20T03:30:51Z Strategic Management of R&D Capabilities with Agent Based Modeling Paul, Jason V. Moser, Bryan R. System Design and Management Program. R&D efforts produce more than just the tangible prototype or patent. These efforts also create intangible attributes that are manifested as a capability, delivering an organization the endogenous skills needed to traverse a technological landscape in a more efficient and successful manner than the competition. The challenge for leadership is aligning resources and policy to balance the tangible and intangible to meet organizational strategy. This is challenging since the intangible outputs are not easily quantifiable. However, Agent Based Modeling (ABM) provides one method to simulate R&D activities to quantify and compare the relative gains between the tangible and intangible outputs. This type of modeling incorporates agents that move about an environment while making decisions based on their interactions with the environment and other agents. Due to the stochastic foundation of this model, a Monte Carlo approach is used and the results are shown as a cumulative distribution function that could allow leadership to compare the relative impacts of different R&D strategies or policies. This work presents a model with agents in the form of researchers that are pursuing an innovation goal. They traverse a technology landscape, ultimately creating a path to realize the innovation goal. The landscape is littered with technical impediments and potential serendipitous discoveries. The researchers must overcome these barriers through individual or collaborative research efforts or avoidance. This model is exercised for self consistency and cross consistency through 3 scenarios to increase confidence. The model is then expanded to include technology areas of lower maturity with higher densities of technical barriers and serendipitous discovery sites and a scenario where researchers conducting basic research work in conjunction with researchers conducting applied research. In these last 2 scenarios, the data highlight the tradespace in a realistic scenario, giving leadership the data to determine best resourcing decisions to achieve organizational strategic goals. Specifically, in both of these scenarios, the time required to achieve the primary innovation goal is greater than the base case but shows a marked increase in organizational knowledge gains and serendipitous discoveries. S.M. 2023-01-19T19:51:57Z 2023-01-19T19:51:57Z 2022-09 2022-10-12T16:05:27.249Z Thesis https://hdl.handle.net/1721.1/147459 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Paul, Jason V. Strategic Management of R&D Capabilities with Agent Based Modeling |
| title | Strategic Management of R&D Capabilities with Agent Based Modeling |
| title_full | Strategic Management of R&D Capabilities with Agent Based Modeling |
| title_fullStr | Strategic Management of R&D Capabilities with Agent Based Modeling |
| title_full_unstemmed | Strategic Management of R&D Capabilities with Agent Based Modeling |
| title_short | Strategic Management of R&D Capabilities with Agent Based Modeling |
| title_sort | strategic management of r d capabilities with agent based modeling |
| url | https://hdl.handle.net/1721.1/147459 |
| work_keys_str_mv | AT pauljasonv strategicmanagementofrdcapabilitieswithagentbasedmodeling |