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Abstract A longstanding goal of evolutionary biology is to understand among-individual variation in resource allocation decisions and the timing of these decisions. Recent studies have shown that investment in elaborate and costly weapons can result in trade-offs with investment in testes. In this study, we ask the following questions: At what point plasticity in resource allocation to these different structures ceases during development, if at all? Furthermore, can individuals tailor their reproductive behavior to accompany structural changes? We experimentally addressed these questions in the insect Narnia femorata, quantifying resource reallocation across development for the first time, using a phenotypic engineering approach. To investigate whether allocation plasticity diminishes throughout ontogeny, we induced weapon loss at a range of different developmental stages and examined subsequent testes mass and reproductive behavior. We found that relative testes mass increased as weapon investment decreased, implying a direct trade-off between testes and weapon investment. However, autotomy postadulthood ceased to induce larger testes mass. Intriguingly, losing a weapon while young was associated with extended adult mating duration, potentially enabling compensation for reduced fighting ability. Our results highlight the importance of examining the ontogeny of trade-offs between reproductive traits and the flexibility of the relationship between reproductive morphology and behavior.
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Deep empirical risk minimization in finance: Looking into the future
Abstract Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over‐training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.
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- Award ID(s):
- 2106462
- PAR ID:
- 10442964
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Mathematical Finance
- Volume:
- 33
- Issue:
- 1
- ISSN:
- 0960-1627
- Page Range / eLocation ID:
- p. 116-145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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