Agent-system co-development in supply chain research: Propositions and demonstrative findings

In this study, we develop an agent-system co-development (ASC) theoretical framework for behavioral research in supply chains. The ASC framework aims at explaining the dynamic agent-system relationships in supply chains whereby both action-influencing properties of human agents (e.g., beliefs, personalities, attitudes) and governance-influencing properties of supply chain systems (e.g., social norms, power-dependence, partnerial/adversarial relationship forms) mutually influence each other over time. Two empirical studies are conducted to illustrate how ASC can be a useful theoretical framework in supply chain research and to partially validate the central thesis of ASC in the contexts of partnerial/adversarial supply chain relationships and cooperative/competitive attitudes of human agents in supply chains. The results of both studies support the central thesis of ASC regarding the dynamic agent-system relationships. From two replicated experiments in Study 1, the results suggest that agents’ cooperative and competitive attitudes in business relationships are altered as they are exposed to different supply chain conditions of partnerial and adversarial relationships. In addition, from the multi-method research efforts in Study 2, the results from two survey studies and an experiment are largely consistent with one another, suggesting that personnel turnovers in existing supply chain systems can eventually lead to changes in supply-chain-system properties including the degrees of long-term commitment, information sharing, and joint problem-solving between supply chain partners, as well as the frequency of opportunism occurrences in the supply chains. Finally, we propound that the dynamic agent-system relationships proposed in the ASC framework can be a useful analytical lens in viewing various supply chain issues, such as supply chain evolutions and changes, supply chain designs and personnel decisions, and self-reinforcing feedback loops and decision tendencies in supply chains.     

Approachability with bounded memory

We study Blackwell's approachability in repeated games with vector payoffs when the approaching player is restricted to use strategies with bounded memory: either strategies with bounded recall, or strategies that can be implemented by finite automata. Our main finding is that the following three statements are equivalent for closed sets. (i) The set is approachable with bounded recall strategies. (ii) The set is approachable with strategies that can be implemented with finite automata. (iii) The set contains a convex approachable set. Using our results we show that (i) there are almost-regret-free strategies with bounded memory, (ii) there is a strategy with bounded memory to choose the best among several experts, and (iii) Hart and Mas-Colell's adaptive learning procedure can be achieved using strategies with bounded memory.     

Stability under learning: The endogenous growth problem

A local dynamic analysis, in the neighborhood of the steady state, is developed for one and two-sector endogenous growth models. The problem differs from the conventionally assumed growth setups because one considers that expectations concerning the next period value of the control variable (consumption) are formed through adaptive learning. In such scenario, the found stability conditions reveal that convergence to the unique steady state point is feasible if a minimum requirement regarding the quality of learning in the long run equilibrium is fulfilled. Therefore, stability of growth under learning is dependent on the efficiency with which expectations are generated.     

重大突发公共卫生事件科技研发应急体系运行机制研究

在突发事件应急管理过程中,科技研发支撑作用十分关键,亟需建立突发公共卫生事件科研应急体系长效运行机制。基于科技研发应急体系的复杂性、适应性特征,从主体属性和体系架构两个层面入手,分析重大突发公共卫生事件科技研发应急体系的复杂适应系统特征,运用复杂适应系统理论模型和动态闭环螺旋模型,研究重大突发公共卫生事件科技研发应急体系运行机制。研究发现,重大突发公共卫生事件背景下,科技研发应急体系具有复杂适应系统的7个基本特征,是典型的复杂适应系统;科技研发应急体系中,科研攻关专家组、诊疗医院、科技部门等科研主体具有各自内部模型,并通过6大标识进行聚集;科技研发应急体系存在事件—需求引导、主体协同、资源交互、成果转化机制。同时,新冠病毒肺炎疫情应对实践表明,疫情防控救治进程中科技研发应急体系存在并遵循上述运行机制。     

Social Capital,Income Diversification and Climate Change Adaptation: Panel Data Evidence from Rural Ethiopia

The choice between specialisation and diversification of income is driven by multiple, interacting factors, such as economies of scale and scope, risk considerations, context, and household characteristics. Using panel data from Ethiopia, we investigate the role of social capital and the covariate risk of climate change and their interaction. We find that households with greater social capital tend to be more specialised, implying that diversification and informal insurance are substitutes in the mitigation of risk. We also find that this effect is significantly weaker in regions more prone to climate change, which is consistent with the average farmer being aware that informal insurance is not an effective protection against risks that affect the entire social network. We use instrumental variable random effects estimation to account for the plausible endogeneity of social capital and we also establish that our results do not depend on the poorest and most constrained individuals in our sample.     

Influence of livelihoods on climate change adaptation for smallholder farmers in the Mekong Delta Vietnam

Farmers in the Vietnamese Mekong Delta face a wide range of climate-related and hydrological factors which threaten rice production. Smallholder farmers must adapt to climate change to sustain rice production as their central and most important livelihood activity. A sample was stratified across agro-ecological areas in the Delta affected by flooding, alluvial soils, acid sulphate soils, and saline water intrusion and by derived farmer typologies. A rural livelihoods approach was used in focus group discussions and in-depth interviews to identify and enumerate enabling and constraining adaptation factors. Smallholders experienced diverse natural hazards such as floods, abnormal rains, high temperatures, water scarcity, and salinity intrusion specific to the agro-ecological areas. Adaptation was constrained by labour shortages, water quality, topography, access to combine harvesters, transportation infrastructure, dryers and household savings. Adaptation was enabled by farming techniques and experience, cooperative groups, water quantity, access to information, and ability to purchase agro-chemicals through credit. Small farmers (< 1?ha) were more constrained than large farmers (> 1?ha) who had an expanded livelihood asset base. A range of policy implications are discussed, but adaptation is not just about technological fixes but requires overall improvements in a range of human, social and financial components.     

LearnSmart,adaptive teaching,and student learning effectiveness: An empirical investigation

Facing the growing number of digital natives entering the classroom, business professors look for innovative ways to enhance the student learning experience. The authors focus on the online interactive learning tool LearnSmart (McGraw-Hill, New York, NY), and examine its impact on student learning effectiveness by testing the direct and indirect relationships among perceived competence, perceived challenge, instructors, perceived value, and satisfaction with LearnSmart. Constructionism served as the theoretical foundation for this study. About 215 students at a public university in the United States took the survey and 197 valid responses were received. Regression analysis results showed that the use of LearnSmart improved students' perceived competency, thus increasing their perceived value of using LearnSmart, as well as their satisfaction with LearnSmart. Perceived value was also found to mediate the impact of perceived competency on satisfaction with LearnSmart, and the instructor played a significant role in facilitating and improving student learning. Perceived challenge impacted student's perceived value of using LearnSmart, but it did not influence satisfaction with LearnSmart.     

Dynamic portfolio optimization across hidden market regimes

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach.     

Know thyself: Leading through the core of you in times of turbulence

The paper describes how leaders behave and react in unprecedented times when a professional service firm has been severely affected by the COVID-19 pandemic. Firsthand data were gathered through interviews, observations, and participation based on direct interaction with leaders and employees. The concept of leadership anatomy is used to describe, discuss, and critique leadership behavior. It signifies the different parts of a human body equipped with sensory ability. The study reveals that in times of crisis, leaders tend to draw on the core of who they are through compassion rather than conventional wisdom in decision making and problem solving. The search for what truly matters helps leaders to reinterpret the ethos of the firm and what they stand for as leaders in their sensemaking of chaos. A deeper reflection of their personal values and beliefs gives them the courage to acknowledge their vulnerability and start seeing the value in others.     

Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model

The rough Bergomi (rBergomi) model, introduced recently in Bayer et al. [Pricing under rough volatility. Quant. Finance, 2016, 16(6), 887–904], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we have designed a novel, hierarchical approach, based on: (i) adaptive sparse grids quadrature (ASGQ), and (ii) quasi-Monte Carlo (QMC). Both techniques are coupled with a Brownian bridge construction and a Richardson extrapolation on the weak error. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method. They reach a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.