In the classic secretary problem, the decision-maker is faced with an online sequence of candidates with values. Upon seeing a candidate, they have to make an irrevocable take-it-or-leave-it decision. We generalize this framework to the secretary problem with two-tier decision-making ( SP-TT ), modeling online selection with hierarchical screening. The problem becomes even more complicated under two-tier decision-making, which captures pervasive real-world scenarios. The first-tier decision-maker ( DM1 ) filters candidates for the second-tier decision-maker ( DM2 ), who must decide irrevocably without knowing future candidates’ scores, except that all scores are bounded within the interval [ m , M ] . The aim is to find a collaborative online strategy between DM1 and DM2 that maximizes the expected return. We prove that no strategy surpasses a competitive ratio of r * and propose a randomized threshold-based strategy ( R T S ) achieving this bound. Our main contribution is introducing a new non-linear programming technique for obtaining and analyzing strategy R T S for SP-TT and its extended variants. We establish a one-to-one correspondence between strategy R T S for SP-TT and the optimal solution to the non-linear program. The extension section enhances the real-world applicability of the framework. Introducing a family of problem variants, including those based on different performance evaluations, data-driven dynamic thresholds considering prior information, inaccuracy scores, and interview costs, we rigorously derive the optimal deterministic and randomized online strategies for each variant. Through simulation experiments, we demonstrate the superior performance of the proposed strategies, particularly compared with the strategy derived from the classic single-stage secretary model, and provide practical recommendations for firm recruitment.

The carbon quota trading problem can be optimized through the design of data-driven online strategies. This study proposes a data-driven framework based on λ-competitive analysis, leveraging historical data from the carbon quota trading market to enhance the performance of online strategies. Traditional strategies tend to be overly conservative under the competitive ratio (or competitive difference). To address this, the paper introduces the concept of the λ-competitive rate (rλ), which combines the competitive ratio and competitive difference, thereby improving strategy performance. Within this framework, the paper introduces the online strategy TBWT(rλ∗) for the carbon quota trading problem and demonstrates its optimality. Furthermore, the special case TBWT(r0∗), an optimal online strategy based on competitive difference, is compared with the previous CDA strategy in the literature. By combining the framework and the strategy TBWT(rλ∗), this paper proposed a data-driven online strategy (DDOS) to solve the carbon quota trading problem. In the final examples, the strategy DDOS utilizes the historical price to calculate trading returns. The results show that the strategy TBWT(rλ∗) achieves higher returns than those based solely on the competitive ratio or competitive difference, while ensuring the robustness and convergence of the strategy.

It is great significance to study what kind of factors can enhance the level of enterprise digital and intelligent transformation (EDIT). Combined with the 'Technology-Organization-Environment' framework (TOE framework) and the dynamic QCA method to study the factors of EDIT and the conditional groupings formed by them. The results found that there are three main groupings affecting the development process of EDIT of manufacturing enterprises, namely: knowledge R&D, organisational R&D, and scale-neutral, and there are no necessary conditions that can realise the EDIT. The conclusions can enrich the research on EDIT and provide useful practical insights for advancing the further EDIT.

Purpose Goal orientation shapes employees’ approach to and interpretation of workplace aspects such as supervisors’ behavior. However, research has not fully examined the effect of goal orientation as an antecedent of abusive supervision. Drawing from victim precipitation theory, this study aims to fill this research gap by investigating how employees’ goal orientation influences their perception of abusive supervision. Design/methodology/approach Two studies were conducted to test the hypotheses. In Study 1, 181 employees in 45 departments participated in the survey, and multilevel confirmatory factor analysis, two-level path model and polynomial regression were used. In Study 2, 108 working adults recruited from a professional online survey platform participated in a two-wave time-lagged survey. Confirmatory factor analysis, hierarchical linear regression and polynomial regression were used. Findings This study found that employees’ learning goal orientation was negatively related to their perception of abusive supervision. In contrast, performance-avoidance goal orientation was positively related to their perception of abusive supervision, whereas performance-approach goal orientation was unrelated to this perception. Moreover, employees’ perception of abusive supervision was greater when learning and performance-approach goal orientation alignment occurred at lower rather than higher levels, and when performance-avoidance and performance-approach goal orientation alignment occurred at higher rather than lower levels. Originality/value This research identified two novel victim traits as antecedents of abusive supervision – employees’ learning goal orientation and performance-avoidance goal orientation. Furthermore, adopting a multiple goal perspective, the authors examined the combined effects of goal orientation on employees’ perception of abusive supervision.

This paper aims to propose a novel framework for designing data-driven strategies for online problems, which utilizes historical data to enhance the actual execution effect of online strategies. It integrates the concepts of competitive ratio and competitive difference to form a comprehensive competitive power concept. Using this framework, a threat-based strategies, TBS lambda (threat-based strategy with preference lambda) is proposed as an optimal strategy in the sense of competitive power for the one-way trading problem, and a data-driven online strategy DDOS (data-driven online strategy) is further proposed. By the discussion of special cases, it is found that TBS0, which is based on competitive difference, is quite different from CDA (competitive difference analysis) in former literature but is also an optimal strategy for the one-way trading in the sense of competitive difference. It is further found that strategy TBS0 performs more aggressively than strategy TBS1 which is based on competitive ratio through special case analysis. Additionally, numerical experiments based on China's carbon emission trading markets are presented to further validate the proposed online strategy. The results demonstrate the superiority of DDOS over the ones based solely on competitive ratio or competitive difference.

Suppose that n persons with their own behavioral characteristics live in a limited area. Some persons here are willing to be kind to others, while others choose to be selfish. Some are penny-pinching, while others may be weathercocks. When two persons meet, they will determine their behaviors according to their own behavior characteristics, and accordingly, obtain their own profits. What kinds of person will succeed in a long run?To discuss the above problem, the agent-based simulation method is used to describe the interaction between persons in grass-roots organizations by using the repeated prisoner’ dilemma model, where the different behavior characteristics of persons are modeled as strategies, and four simulation models with no manager, strongly supervised managers, weakly supervised managers, and no supervised managers are constructed, respectively.In the no manager model, we design 12 strategies to depict the behavior of different persons and find that the “cunning men” get the highest profits. The reason may be that the persons of this strategy can not only ensure that they will not be retaliated by selfish persons but also benefit from friendly ones. It implies that no manager mechanism is not a suitable form for grass-roots organizations.In the models with managers, managers are elected. Three sub-models are discussed below. In the model with strongly supervised managers, managers must always cooperate in the interaction with others. In this case, the “good men” with the always-cooperation-strategy, who are always cooperative with others, get the highest profits. It is may be that the election mechanism provides them with an important opportunity to be elected as managers to improve their profits. In the model with weakly supervised managers, some elected managers may choose to betray, while others still cooperate. Therefore, each strategy is changed into two strategies, for example, the always-cooperation-strategy is changed into two strategies: the really-always-cooperation-strategy, who is still cooperative after being elected as a manager, and the like-always-cooperation-strategy, who will betray after becoming a manager. The reason may be that although choosing betrayal can obtain high returns in a short period of time, the election mechanism makes it difficult for such agents in the future election, and as a result, the “good men” with the really-always-cooperation-strategy get the highest profits in this case. In the model with no supervised managers, some elected managers may not only choose to betray but also even encroach on the profits of others during their tenure. Although it is likely to lose the future election, elected managers still may have accumulated a lot of wealth by way of encroachment, which may be why the “hypocrites” with the like-always-cooperation-strategy get the highest profits in this case. A good mechanism should be able to protect the interests of “good persons”. Therefore, from the perspective of individuals, the best mechanism is with strongly supervised managers, the second is with weekly supervised managers, the third is with no managers and the last is with no supervised managers. Furthermore, from the perspective of organizations, we compare the average profits of all persons under different mechanisms, and also find that the best mechanism is with strongly supervised managers, the second is with weekly supervised managers, the third is with no managers and the last is with no supervised managers. Whether from the perspective of individuals or organizations, the mechanism with supervised managers, especially with strongly supervised managers, is the most efficient organizational form since it can protect the interests of “good persons” and thus lead people to being “good”.

In this paper, we study a multiple time series search problem in which at the first n periods, one product is produced in each period and becomes sellable. The total length of the trading horizon N (N > n), i.e., the total number of trading periods (which includes the first n periods when the products are produced), is unknown beforehand. All the n products are homogeneous. At each period, a price is observed and the player must decide immediately the number of available products to sell at this period, without the knowledge of future prices and when the trading horizon ends. The objective is to maximize the total revenue from selling the n products. We present an online algorithm ON for this problem and prove its competitive ratio. A lower bound on the competitive ratio for this online problem is also proved. Numerical results for the theoretical competitive ratio of algorithm ON and the lower bound are also reported.

The paging problem is that of deciding which pages to keep in a memory of k pages in order to minimize the number of page faults in a two-level store. It is a special case of the famous k-server problem. In this paper, we firstly show that WFA is kM/m-competitive for the k-server problem where the distances between two points are all in [m,M] (0<m≤M), and thus WFA is k-competitive for the paging problem. And we further show that the well known FIFO for the paging problem is just a special case of WFA.

本文从一个考虑突变的间接互助模型出发,运用基于Agent的建模技术,从社会发展演变的角度,仿真了感恩和讹诈两种行为,并探讨其影响.若仅将感恩行为纳入考量,则发现感恩能促使一个考虑突变的社会重新演化成一个友善型社会.但是,如果把受助者的讹诈行为引入后,发现讹诈造成了整个社会的冷漠,而且一味强调感恩并不能完全消除讹诈事件的冲击,确保受害者对社会友好程度不受讹诈事件的影响,才是社会健康演进的关键.

采用由熵值法和均等赋权构成的组合赋权,基于发展的基本面、社会成果和生态成果三个维度,测度2004-2017年黄河流域77个地级以上城市经济高质量发展水平.进而使用Kernel密度估计和Markov链分析黄河流域上、中、下游城市的动态研究规律和特征,并对其发展趋势进行预测.研究发现:(1)黄河流域城市经济高质量发展指数密度分布曲线的中心均逐渐右移,并表现出右拖尾,其密度函数呈偏态分布,表明黄河流域城市经济高质量发展呈上升态势,但城市间具有非平衡发展特征.(2)传统Markov链分析显示,黄河流域城市经济高质量发展保持稳定的概率至少为50.2％,同时也存在马太效应.(3)空间Markov链分析表明,城市经济高质量发展水平发生转移的概率存在空间依赖性.因此通过协同推进大治理的机制创新,驱动黄河全流域经济高质量发展具有重要意义.

The k-server problem was introduced by Manasse et al. (in: Proceedings of the 20th annual ACM symposium on theory of computing, Chicago, Illinois, USA, pp 322–333, 1988), and is one of the most famous and well-studied online problems. Koutsoupias and Papadimitriou (J ACM 42(5):971–983, 1995) showed that the work function algorithm (WFA) has a competitive ratio of at most $$2k-1$$ for the k-server problem. In this paper, by proposing a potential function that is different from the one in Koutsoupias and Papadimitriou (1995), we show that the WFA has a competitive ratio of at most $$n-1$$, where n is the number of points in the metric space. When $$n<2k$$, this ratio is less than $$2k-1$$.

对于在线时间序列搜索问题,在假设对未来信息有一定的预期下,提出了在线时间序列搜索的风险补偿模型,进一步研究了模型的求解,给出了模型的一个最优策略,并通过数值计算讨论了最优策略的补偿函数随参数变化规律.数值实验结果表明,随着风险容忍度的增大与预期区间下限的增大,补偿函数均增大且趋于收敛;随着预期概率的增大与预期区间上限的减少,补偿函数分别增大.研究结果丰富了在线时间序列搜索的理论且具有实际应用价值.

This work investigates an online two stage k-search problem where an online player makes selections in two stages. In the first stage a number of more than k quoted prices are selected as candidates, and then exactly k highest quoted prices are chosen from the candidates in the second stage. The objective is to maximize the total profit of the k final accepted prices. We mainly propose a deterministic online algorithm and prove that it is optimal in competitiveness. A further discussion is given considering various relationships between the value of k and the number of candidates.

This work proposes an online (J,K)-search problem where an online player has K units of some asset for selling and has to sell at least J≤K units of the asset in a finite number of periods. At the beginning of each period a quoted price is observed and the player has to decide immediately and irrecoverably whether to accept the price as well as the amount of the asset to be sold at the price. The objective is to maximize average selling price. We present two models where at most one unit of the asset can be sold in each period and where one or more units of the asset can be sold in each period. For both models we propose optimal online deterministic algorithms.

We discuss the P versus NP problem from the perspective of addition operation about polynomial functions. Two contradictory propositions for the addition operation are presented. With the proposition that the sum of k (k<=n+1) polynomial functions on n always yields a polynomial function, we prove that P=NP, considering the maximum clique problem. And with the proposition that the sum of k polynomial functions may yield an exponential function, we prove that P!=NP by constructing an abstract decision problem. Furthermore, we conclude that P=NP and P!=NP if and only if the above propositions hold, respectively.

The traditional VWAP trading strategy trading volume depends on the distribution of split single trans -action, and the forecast of the distribution of trading volumes is based on the interval volume proportion of the total volume , and this prediction method does not consider the stock price changes .Therefore , by means of the time series factor decomposition the paper first predicts the distribution of trading volumes , then based on the stock price changes in the distribution of trading volumes adjust the trading volume distribution of dynamic ranges and builds the stocks selling strategy by the trading volume distribution , and finally , through an empirical test in this paper , tests the predictive validity and effectiveness of the distribution of trading volumes .Numerical results show that the prediction method of the distribution of dynamic volumes given in this paper is better than the tradi -tional VWAP method and the results of the new method are much closer to the distribution of the actual transac -tion volume .And compared with the traditional VWAP trading strategy , the strategy given in this paper is more gainful.

The online theory is used to study multi-stock algorithmic trading strategy .On the basis of El-Yaniv’s research , online buying strategy is established and proved to be the optimal online strategy;multi-stock algorith-mic trading strategy is designed and the investment portfolio is determined by weighting every stock yield with applying single stock trading strategy into multi-stock trading strategy .Transaction time data of twenty stocks , which are picked out of the A Stock of Shanghai Stock Exchange , is selected to test and verify the validity of the strategy mentioned in this paper .Ten stocks are randomly picked out of these twenty stocks to compose a group , and four groups are selected to be tested respectively , and the result indicates that the strategy proposed in this paper has better yield to any multi-stock.As for transaction cycle, ten even length is selected for test and the result implies the average yield will reach its maximum when the transaction cycle is eighteen and the average yield is 5.2%.

The basic models of online time series search and one-way trading are introduced by El-Yaniv et al. in Algorithmica 30(1), 101–139 (2001) where it is assumed that the prices are bounded within interval [m,M] (0<m<M). In this paper, we consider another case where every two consecutive prices are interrelated, that is, the variation range of each price depends on its preceding price. We present optimal deterministic online algorithms for the two problems, respectively. According to one conclusion in Algorithmica 30(1), 101–139 (2001), we further point out that for the case we considered, an optimal deterministic algorithm for the one-way trading problem can be regarded as an optimal randomized one for the time series search problem, and randomization is useless for the one-way trading problem.