Innovation and informal knowledge exchanges between firms

• URL: http://arxiv.org/abs/2208.14719v1
• Date: Wed, 31 Aug 2022 09:26:10 GMT
• Title: Innovation and informal knowledge exchanges between firms
• Authors: Juste Raimbault
• Abstract summary: We propose a stylised agent-based model to test the role of geographical proximity and informal knowledge exchanges between firms on the emergence of innovations. Sensitivity analysis and systematic model exploration unveil a strong impact of interaction distance on innovations. Model bi-objective optimisation shows a compromise between innovation and product diversity, suggesting trade-offs for clusters in practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Firm clusters are seen as having a positive effect on innovations, what can be interpreted as economies of scale or knowledge spillovers. The processes underlying the success of these clusters remain difficult to isolate. We propose in this paper a stylised agent-based model to test the role of geographical proximity and informal knowledge exchanges between firms on the emergence of innovations. The model is run on synthetic firm clusters. Sensitivity analysis and systematic model exploration unveil a strong impact of interaction distance on innovations, with a qualitative shift when spatial interactions are more intense. Model bi-objective optimisation shows a compromise between innovation and product diversity, suggesting trade-offs for clusters in practice. This model provides thus a first basis to systematically explore the interplay between firm cluster geography and innovation, from an evolutionary perspective.

Related papers

• MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities [72.68829963458408]
  We present MergeNet, which learns to bridge the gap of parameter spaces of heterogeneous models. The core mechanism of MergeNet lies in the parameter adapter, which operates by querying the source model's low-rank parameters. MergeNet is learned alongside both models, allowing our framework to dynamically transfer and adapt knowledge relevant to the current stage.
  arXiv  Detail & Related papers  (2024-04-20T08:34:39Z)
• Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks [42.39389192863717]
  We propose Prismatic, a visual analytics system that integrates quantitative analysis of historical performance and qualitative analysis of business relational knowledge to cluster correlated businesses interactively.
  arXiv  Detail & Related papers  (2024-02-14T06:47:30Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [55.65482030032804]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. Our approach infers dynamically evolving relation graphs and hypergraphs to capture the evolution of relations, which the trajectory predictor employs to generate future states.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters [4.8051028509814575]
  We propose a SIM-GAT model to predict visitation flows between community business clusters and their trade areas. A graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the clusters and interdependencies of business.
  arXiv  Detail & Related papers  (2024-01-09T23:42:21Z)
• Feature Interaction Aware Automated Data Representation Transformation [27.26916497306978]
  We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection. We reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making.
  arXiv  Detail & Related papers  (2023-09-29T06:48:16Z)
• Deep Learning-based Analysis of Basins of Attraction [49.812879456944984]
  This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field.
  arXiv  Detail & Related papers  (2023-09-27T15:41:12Z)
• A new perspective on the prediction of the innovation performance: A data driven methodology to identify innovation indicators through a comparative study of Boston's neighborhoods [0.0]
  The study uses a large geographically distributed dataset across Boston's 35 zip code areas. In order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations.
  arXiv  Detail & Related papers  (2023-04-04T05:45:50Z)
• Convolutions for Spatial Interaction Modeling [9.408751013132624]
  We consider the problem of spatial interaction modeling in the context of predicting the motion of actors around autonomous vehicles. We revisit convolutions and show that they can demonstrate comparable performance to graph networks in modeling spatial interactions with lower latency.
  arXiv  Detail & Related papers  (2021-04-15T00:41:30Z)
• Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations [51.8796674904734]
  We propose a new cooperative learning framework with pre-trained heterogeneous observation representations. We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
  arXiv  Detail & Related papers  (2020-12-24T04:52:29Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.