The Formation of Production Networks: How Supply Chains Arise from Simple Learning with Minimal Information
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917995040735232 |
|---|---|
| author | Vu, Tuong Manh Carrella, Ernesto Axtell, Robert Guerrero, Omar A. |
| author_facet | Vu, Tuong Manh Carrella, Ernesto Axtell, Robert Guerrero, Omar A. |
| contents | We develop a model where firms determine the price at which they sell their differentiable goods, the volume that they produce, and the inputs (types and amounts) that they purchase from other firms. A steady-state production network emerges endogenously without resorting to assumptions such as equilibrium or perfect knowledge about production technologies. Through a simple version of reinforcement learning, firms with heterogeneous technologies cope with uncertainty and maximize profits. Due to this learning process, firms can adapt to shocks such as demand shifts, suppliers/clients closure, productivity changes, and production technology modifications; effectively reshaping the production network. To demonstrate the potential of this model, we analyze the upstream and downstream impact of demand and productivity shocks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Formation of Production Networks: How Supply Chains Arise from Simple Learning with Minimal Information Vu, Tuong Manh Carrella, Ernesto Axtell, Robert Guerrero, Omar A. Multiagent Systems Machine Learning General Economics Economics We develop a model where firms determine the price at which they sell their differentiable goods, the volume that they produce, and the inputs (types and amounts) that they purchase from other firms. A steady-state production network emerges endogenously without resorting to assumptions such as equilibrium or perfect knowledge about production technologies. Through a simple version of reinforcement learning, firms with heterogeneous technologies cope with uncertainty and maximize profits. Due to this learning process, firms can adapt to shocks such as demand shifts, suppliers/clients closure, productivity changes, and production technology modifications; effectively reshaping the production network. To demonstrate the potential of this model, we analyze the upstream and downstream impact of demand and productivity shocks. |
| title | The Formation of Production Networks: How Supply Chains Arise from Simple Learning with Minimal Information |
| topic | Multiagent Systems Machine Learning General Economics Economics |
| url | https://arxiv.org/abs/2504.16010 |