Effects of trust-based decision making in disrupted supply chains

The United States has experienced prolonged severe shortages of vital medications over the past two decades. The causes underlying the severity and prolongation of these shortages are complex, in part due to the complexity of the underlying supply chain networks, which involve supplier-buyer interactions across multiple entities with competitive and cooperative goals. This leads to interesting challenges in maintaining consistent interactions and trust among the entities. Furthermore, disruptions in supply chains influence trust by inducing over-reactive behaviors across the network, thereby impacting the ability to consistently meet the resulting fluctuating demand. To explore these issues, we model a pharmaceutical supply chain with boundedly rational artificial decision makers capable of reasoning about the motivations and behaviors of others. We use multiagent simulations where each agent represents a key decision maker in a pharmaceutical supply chain. The agents possess a Theory-of-Mind capability to reason about the beliefs, and past and future behaviors of other agents, which allows them to assess other agents’ trustworthiness. Further, each agent has beliefs about others’ perceptions of its own trustworthiness that, in turn, impact its behavior. Our experiments reveal several counter-intuitive results showing how small, local disruptions can have cascading global consequences that persist over time. For example, a buyer, to protect itself from disruptions, may dynamically shift to ordering from suppliers with a higher perceived trustworthiness, while the supplier may prefer buyers with more stable ordering behavior. This asymmetry can put the trust-sensitive buyer at a disadvantage during shortages. Further, we demonstrate how the timing and scale of disruptions interact with a buyer’s sensitivity to trustworthiness. This interaction can engender different behaviors and impact the overall supply chain performance, either prolonging and exacerbating even small local disruptions, or mitigating a disruption’s effects. Additionally, we discuss the implications of these results for supply chain operations.


Introduction
We are extremely grateful to all reviewers for their comments and suggestions that helped improving the overall clarity of the paper. We have revised the entire manuscript in order to address all those comments and globally improve the readability and clarity of the presentation and discussions.
Below we provide detailed replies to the reviewers' comments and explain how the different suggestions were addressed and incorporated in the revised manuscript. Reviewers' comments are quoted in a shaded box, followed by our reply in regular font. We also emphasize in bold how the different issues pointed out by the reviewers were addressed in the revised version of the manuscript.
Additionally, please find attached a marked-up copy of the manuscript that highlights changes made to the original version.

It is good writing paper and is suitable for publications in PLOS ONE.
We would like to thank the reviewer for the thoughtful comments.

Reviewer #2
The manuscript is interesting and technically sound.
However, the English usage of the submitted paper need to be further polished, a careful reading of a native English speaker is necessary.
We would like to thank the reviewer for the thoughtful comments.
The paper has been thoroughly proofread.

Reviewer #3
This paper is more interesting and now is presented in a good format. Still, it could be better to consider more scenarios for obtaining more comprehensive insights. If my concern is considered this paper can be accepted for publication.
First, we would like to thank the reviewer for the thoughtful comments.
Regarding the comment about more scenarios to consider, we added a new scenario with a different disruption profile starting on page 12 of the manuscript. In this new scenario we examine how the overall supply chain cost trajectory changes with changes in disruption parameters. This resulted in uncovering new insights about nonlinear relationship between supply chain cost with the length of disruption, when the total size of the disruption is held constant.
We also ran extra robustness simulations for scenarios 4 and 5. In these robustness simulations we consider stochastic patient demand and examine how using trustworthiness by one of the healthcenters versus not using trustworthiness by any of the healthcenters affects supply chain agents' cost.

Reviewer #4
This paper reports on a multi-agent system for analyzing (combined) trust-based decisions and disruption impact on supply chain performance, cost for instance. The paper is well written and the explanation of the model is quite clear. The analysis of the example is also quite sound.
My only remark relates to the positioning of this research with regard to existing literature, I would suggest breaking down the introduction into two parts: 1) introduction and 2) state of the art and positioning. A quick search in two data bases pointed the following titles of published research works, which can benefit the improvement of the state of the art: • A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain • An adaptive multi-agent system for cost collaborative management in supply chains • Analysis of the performance of supply chains configurations using multi-agent systems • Customer order fulfilment in mass customization context -An agent-based approach