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Historical stock prices have long been used to evaluate a stock’s future returns as well as the risks associated with those returns. Similarly, historical dividends have been used to evaluate the intrinsic value of a stock using, among other methods, a dividend discount model. In this chapter, we propose an alternate use of the dividend discount model to enable an investor to assess the risks associated with a particular stock based on its dividend history. In traditional applications of the dividend discount model for stock valuation, the value of a stock is the net present value of its future cash dividends. We propose an alternative approach in which we calculate the internal rate of return for a stream of future cash dividends assuming the current stock price. We use a bootstrapping approach to generate a stream of future cash dividends, and use a Monte Carlo simulation approach to run multiple trials of the model. The probability distribution of the internal rates of return obtained from the simulation model provides an investor with an expected percentage return and the standard deviation of the return for the stock. This allows an investor to not only compare the expected internal rates of return for a group of stocks but to also evaluate the associated risks. We illustrate this internal rate of return approach using stocks that make up the Dow Jones Industrial Average.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

To overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.

Firstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.

This study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.

This study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.

Initially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

The purpose of this paper is to clarify the ontological assumptions regarding the concept of agency and sociality within business networks in the Industrial Marketing and Purchasing Group (IMP) research by refining these assumptions with a relational sociological (RS) perspective. This paper reinforces the robustness of the actors-resources-activities (ARA) model with an in-depth investigation of the actor dimension, where local interactions between interdependent individuals play a central role in building common futures within business networks through organisational reflexivity.

This conceptual paper investigates the social ontology of research. It challenges the implicit assumptions of IMP research regarding agency and sociality within business networks with a problematisation strategy (Sandberg and Alvesson, 2011). Combining IMP views on agency with the RS perspective, it sets this combined framework as an alternative for the analysis of sustainability and ethics within business networks.

Combining IMP research and an RS perspective allows us to extend the knowledge of sociality within business networks, highlighting the centrality of meaning sharing in the process of network change. By focusing on symbolic interaction processes, an RS perspective contributes to a deeper theoretical understanding of the relationship between local communication and business network patterns. Combined with an IMP perspective on agency, it provides researchers with an alternative conceptual framework for examining sustainability by considering ethics and leadership dialectically.

RS is still an emerging stream within sociology, characterised by diverse views. Not all relational sociologists, as scientists, feel obliged to engage with sustainability research. Thus, the paper is a two-sided invitation to IMP researchers and relational sociologists to delve into the adaptation processes in business networks in highly uncertain environments.

RS focusing on the centrality of communication in local interactions, business network researchers can show that organisational leaders are not the ones with a charismatic vision isolated from any natural and social environment; rather, they are the people with “the capacity to assist the group to continue acting ethically, creatively and courageously in the unknown” (Stacey,2013).

Adopting an RS perspective on agency in business networks can help managers and researchers determine how business networks can be managed in a more sustainable way. Combined with a dialectical and processual understanding of ethics, the IMP-RS perspective emphasises day-to-day local communication practices within and between organisations that challenges microeconomic views on nature, strategy, ethics and leadership. This paper thus places the social at the centre of sustainability approaches.

From an RS perspective, business networks are analysed as patterns of interactions between many organisations and individuals. The value of this conceptual paper is in showing that change within business networks is negotiated through local interactions and symbolic communication between individuals. Thus, it suggests the need to combine the individual and the organisational levels to analyse agency within business networks and to examine the adaptation of business networks to sustainability.

Je crois devoir vous donner connaissance dès l'abord, de la façon dont un groupe d'experts fran‐çais a voulu traiter le rapport de base que je leur avais fait réserver.

This study aims to specify whether heterogeneous reference-point-based aspirations are related to the cooperation levels of R&D alliance portfolios in a positive or negative (or nonlinear) way, and to unveil how cooperative behaviors evolve in recurrent project cooperation.

This study establishes a network containing a cooperation subnetwork and a project subnetwork based on patent data in the “deep learning” field to investigate how cooperative behaviors evolve in R&D alliance portfolios. A model of evolutionary games on complex networks is constructed to gain insight into the dynamic evolution of DMs’ strategies.

First, the heterogeneous aspirations of DMs can improve the cooperation level in R&D alliance portfolios. Second, compared to prudent DMs, aggressive DMs are more likely to choose the cooperation strategy, implying that an appropriate aspiration level nurtures cooperative R&D endeavors with partners. Third, the effects of effort complementarity, knowledge reorganization capabilities and cooperation supervision on cooperation are contingent on the distribution of DMs’ aspiration types.

Policymakers should identify aspiration types of DMs when screening partners. They can encourage partners to focus more on historical payoffs and establish relatively higher aspiration levels to improve the cooperation level. Developing highly detailed contracts becomes crucial when cooperating with firms that possess extensive knowledge reorganization capabilities.

This work contributes a theoretical framework for investigating cooperation in R&D alliance portfolios through the lens of evolutionary games on complex networks, thus revealing the effects of heterogeneous reference-point-based aspirations of DMs on R&D cooperation.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.