“双创”时代高校创客空间的复杂适应系统分析及优化策略

“大众创业、万众创新”时代,创客空间建设已成为高校扶持大学生创业的主流模式和重要举措。基于复杂适应系统（CAS）理论,阐释了高校创客空间的开放性、非线性、自组织性和涌现性等系统特征,指出高校创客空间的演进过程包括接触和交互两个阶段,在其演进过程中,标识机制和积木机制扮演着重要角色。最后,从CAS视角提出促进高校创客空间发展和系统优化的建议。     

多主体建模下四类创新文化特征对创新的影响

创新文化具有自组织性和开放性,是一个知识创造和利用的复杂适应系统。基于复杂适应系统视角,将创新文化特征分为冒险开拓、创新氛围、交流共享、决策参与4类,通过构建多主体（Agent）创新文化复杂系统模型,深入探讨了4类创新文化对创新的影响机制。仿真结果表明,4类创新文化特征对创新的影响存在差异。其中,冒险开拓、创新氛围、交流共享3类创新文化均对创新存在正向促进作用,而决策参与对创新的促进作用与其它3个创新文化特征具有显著关系,且冒险开拓与交流共享等因素在协同作用下比单一因素对创新的影响更积极、更显著。     

Vulnerability assessment of water resources to climate change in Chinese cities

Climate change is an inevitable trend,which challenges security of water resources in China,especially in cities.Assessing vulnerability of water resource to climate change in cities has important role for policy makers. The paper constructs a vulnerability function,including exposure,sensitivity and adaptive capacity,according to the vulnerability concept proposed by IPCC,establishes an assessment indicators system of water resources to climate change in cities,and analyzes vulnerability features of Chinese cites based on 655 cities＇data in 2006.The vulnerability assessment results show that there are distinctive differences among all the cities,between east,central and west cities,between ordinary,big and mega cities,while there is no statistical significant difference between north and south cities.Based on the research,the paper suggests that strategic emphasis should focus on the central cities and ordinary cities     

基于前后轮侧偏角的汽车自动转向头灯转角计算及联合仿真研究

由于汽车前照灯转角和汽车的运动状态有关,而汽车的运动状态又受轮胎侧偏特性的影响,因此本文利用前后轮侧偏角的绝对值之差来修正前照灯理论转角;然后,我们在simulink中建立步进电机仿真模型,在ADAMS中建立前照灯减速机构虚拟样机模型,再通过两者组合成前照灯转角开环控制系统,并把前照灯理论转角输给该开环控制系统,得出了前照灯的实际输出转角对需求理论转角的跟随曲线。     

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.     

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.