SCI论文发表很容易【3】：论文修改稿-如何反驳审稿人

3.1 反驳（回复）审稿人的意见

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①这一个用于回答审稿人对模型的反驳：

The characteristics of knowledge sharing and learning in our model align with existing research findings. We model continuous distribution of knowledge, with a farmer’s post-acquisition knowledge being contingent upon their original endowments. Our study distinguishes itself from prior research in both scope and approach. We examine a distinct group of smallholders within emerging markets, employing analytical models as opposed to relying on empirical studies.

②对关键假设的反驳

We assume that knowledge levels can be numerically ordered. The assumption of unidimensional knowledge is strong, yet it facilitates deriving first-order insights when initially exploring many-to-many knowledge-sharing processes. We will address limitations and outline future research in the conclusion.

用于突出本文模型与之前最相近的一篇文章的区别（更好地彰显文章创新点）：

Our paper is fundamentally different from Chen et al. (2015) in four aspects.

By focusing on a more general sharing mode, we address the issue of knowledge dissemination in large-scale distributed systems. Chen et al. (2015) proposed a one-to-many knowledge sharing mechanism.

In a sharing mode where knowledge transfer is unidirectional, only one party possesses the expertise to share, while the others remain passive recipients. This vertical learning mechanism is characterized by agents deriving knowledge directly from a principal. Conversely, we examine a many-to-many sharing model, wherein each farmer can simultaneously act as both a knowledge provider and a learner. This horizontal sharing dynamic constitutes the fundamental feature of decentralized knowledge-sharing platforms like P2P networks.

Second, we address a variety of critical sharing challenges that do not emerge in the one-to-many knowledge-sharing framework analyzed in Chen et al. (2015). These challenges encompass which farmers are inclined to share voluntarily and how an individual farmer’s sharing behavior impacts others’ decisions to share.

Third, our results are more general than those of Chen et al. (2015). Although Chen et al. (2015) and our study both demonstrate that voluntary knowledge sharing exists but is suboptimal, the underlying assumptions leading to this conclusion differ. While Chen et al. (2015) base their result on the existence of an outside expert occasionally answering farmers' questions on the platform and a minimum output requirement to facilitate market transactions, our conclusion is derived solely from the assumption that farmers are competing in selling two grades of crops.

Fourth, due to the insufficient nature of voluntary knowledge sharing, Chen et al. (2015) have failed to offer a mechanism that can enhance sharing. On the contrary, we advocate an incentive-based mechanism with low implementation costs that can encourage farmers to share voluntarily up to the efficient sharing level.

⑤为何没有研究农民的异质性

Assuming that all farmers exhibit identical learning capacities h within the platform, we posit that their learning abilities remain consistent. However, when farmers’ learning capacities exhibit heterogeneity (i.e., h(k)), the analysis becomes intractable, thus necessitating its postulation in future research.

⑥模型与现实场景的结合

Note that, although the assumption of "learning from the best" may seem strong, it is reasonable in some real-world applications, such as the Rainforest Alliance's SCN project and WeFarm. In the former case, Rainforest Alliance selects the two best shared management practices for irrigation, composting tips, and others. Thus, farmers will draw lessons from the best practices selected by Rainforest Alliance. In the latter case, farmers are empowered to validate users and information and to rate content they receive. WeFarm assigns key words, locations, and profiles to ensure it is constantly improving and learning in order to deliver the best quality content possible to its users. WeFarm also uses numerous filters and processes to ensure the advice shared is of the highest quality, as stated by Kenny Ewan, the chief executive officer of WeFarm, in an interview with This Is Money (Lawrie 2016).

⑦扩展后结论仍然成立

Upon a closer examination of the proof for the reward mechanism, it is evident that all results related to the reward mechanism remain valid when B<0. In fact, provided that farmers are infinitesimal and their sharing decisions are continuous, our quota-based reward mechanism would be applicable to induce sharing up to the efficient shared knowledge level t (c). This is because, when deciding whether to share knowledge, farmers evaluate the changes in their market payoffs and the rewards they can expect to receive.

⑧研究结论可以作为模板学习

This paper presents a pioneering effort to elucidate the dynamics of knowledge-sharing and learning among smallholder farmers. We offer a theoretical framework to analyze the economic motivations underlying knowledge-sharing and non-sharing behaviors. We observe that high-knowledge farmers exhibit a disinclination to share, due to the fact that sharing knowledge enhances others' productivity and increases the competition in selling.

the high-quality crop.

By observing that farmers' voluntary shared knowledge level is insufficient to maximize farmer welfare, we demonstrate that NGOs can enhance farmer welfare by encouraging farmers to share the appropriate level of knowledge. To achieve this, we propose a straightforward quota-based reward mechanism that effectively incentivizes farmers to share the right amount of knowledge at a low cost. Our theoretical insights may serve as testable hypotheses for exploring the economic incentives behind knowledge-sharing and reward mechanism design. Given that this study represents an initial exploration of knowledge exchange among smallholder farmers, there are numerous opportunities for expanding our findings. First, we assumed that knowledge is unidimensional, relying on a single attribute. It would be valuable to investigate farmers' knowledge-sharing behavior in a multidimensional knowledge framework. Second, in our analysis, we assumed that farmers only learn from the highest level of knowledge shared. In reality, farmers might learn from the average knowledge level or even random knowledge levels, particularly when platforms do not interfere with learning processes. It would be interesting to examine how different learning mechanisms influence knowledge-sharing outcomes. Third, while we focused on intangible resources, farmers in developing economies might also share tangible resources such as water, labor, irrigation systems, or farming tools. On one hand, common tangible resources could limit farmers' willingness to share knowledge, as farmers with higher knowledge levels tend to produce more and utilize more resources. On the other hand, higher knowledge levels might improve the efficiency of resource utilization. Therefore, it would be worthwhile to investigate how shared resources impact farmers' knowledge-sharing behavior. We have left these potential extensions for future research.

3.2 一些隐晦的话

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