**2. Theoretical Background**

Our study is concerned with analyzing the impact of making a systematic bad decision (for the estimation of DSMBD) in AGM, when deciding if the financial resources are directed through dividends or for investments in the company. We propose an agent-based model, which is implemented in NetLogo 6.0.4. Below, the theoretical background related to this issue is presented.

The literature regarding bad decisions made by individuals is vast (e.g., Shefrin and Statman 1985; De Bondt and Thaler 1995; Odean 1998; Rubinstein 2001; Hirshleifer 2001; Campbell et al. 2009; Gennaioli et al. 2015; Morgan and Hansen 2006; Dragotă 2016; Pikulina et al. 2017). In this paper, we consider the impact of making systematically bad decisions in connection to setting a dividend policy. Dividend policy, respectively fixing the portion of the net earnings paid to shareholders, can be an interesting application for analyzing DSMBD. A dividend policy can be considered as optimal from some perspectives, but non-optimal from others. For instance, paying dividends can be a sign of the companies' interest of protecting the shareholders' rights (La Porta et al. 2000b), but can also be interpreted as a smaller amount of financial resources for adopting profitable investment projects. For this reason, the discussions in AGM can determine different solutions, which can be optimal, or, to the contrary, wrong, even if they are made with very good intentions and based on rational arguments (sometimes, with sound foundations in financial literature). The harmonization between these contradictory viewpoints can be impossible. For this reason, some agents can be convinced that their decisions are good and can persist in making bad decisions.

At the AGM, shareholders decide the amount that will be paid as dividend. The decision regarding dividend payment consists in fixing a percent *p* from the net earnings (dividend payout ratio, DPR) to be paid as dividend (a percent equal to (1 <sup>−</sup> *p*) is allocated for investments)1. Shareholders are different from many perspectives. A large amount of financial literature analyzes the relations between large (controlling) shareholders and minority shareholders (Shleifer and Vishny 1986; Holderness 2003). Many papers analyze the controlling shareholders' behavior in connection with dividend policy, mainly related to agency problems (Easterbrook 1984; La Porta et al. 2000b; Fidrmuc and Jacob 2010). Additionally, in their decision, shareholders are (or at least, can be) influenced by the managers proposals regarding the dividend proposed to be paid. Usually, they approve (or not) DPR proposed by management. This can determine also agency costs (Jensen and Meckling 1976). Another segmentation of shareholders in different groups can be made considering some socio-cultural factors. Some papers provide evidence between dividend policies across the world, considering different socio-cultural as determinants of dividend policy (Fidrmuc and Jacob 2010; Shao et al. 2010; Ucar 2016). As an effect, a segmentation in different groups (dominant or not) can be useful in modelling shareholders' behavior.

Many papers are concerned about dividend policy. Dividend policy is approached from many perspectives (Graham and Dodd 1951; Walter 1956; Miller and Modigliani 1961; Miller and Scholes 1978; Bhattacharya 1979; Kalay 1980; Easterbrook 1984; La Porta et al. 2000a, 2000b; Fidrmuc and Jacob 2010; Shao et al. 2010; Ucar 2016; Jiang et al. 2017, etc.). Some classical papers propose rules in recommending an optimal dividend payout (Graham and Dodd 1951; Walter 1956). Other studies identify the factors that influence the dividend payment (Lintner 1964; La Porta et al. 2000a; Fidrmuc and Jacob 2010; Jiang et al. 2017). Miller and Modigliani (1961) prove, under some restrictive assumptions, the irrelevance of dividend payout on shareholders' wealth. Different real-life factors challenge the implications of Miller and Modigliani theorem: agency problems (Easterbrook 1984; La Porta et al. 2000a), behavioral and cultural influences (Shao et al. 2010; Ucar 2016), imperfect information and signaling effects (Bhattacharya 1979; Kalay 1980), taxation (Miller and Scholes 1978; Hanlon and Hoopes 2014), etc.

Many papers provide recommendations regarding an optimal dividend policy, contrary to the irrelevance theorem of Miller and Modigliani (1961). For instance, Walter(1956) proposes that dividends should be paid only in the case in which the company can't offer to its shareholders a rate of return higher than the required rate of capital. Other papers suggest that dividends should be paid if the company is interested by the minority shareholders' interests (Graham and Dodd 1951; La Porta et al. 2000b). The list of papers providing more or less advices regarding an optimal dividend policy also provide a multitude of divergent advice: pay, do not pay, or it does not matter. Thus, the literature on dividend policy does not produce a consensus for an optimal dividend policy. For this reason, we can reasonably assume that, even if they are convinced that their arguments have sound theoretical foundations, some agents will make, depending by case, a bad decision, and they would not be able to consider their decision is bad. Practically, they will not be able to characterize their decisions as being bad. This situation will conduce to making systematically bad decisions. It is very possible (and reasonably plausible) to appear a segmentation between two classes of agents, respectively the ones that are making good decisions (being convinced that they are making a good decision) and the ones that are making a bad decision (but being also convinced that they are making a good decision).

Over the last years, applications using agent-based modelling have been successfully used in various research fields such as: supply chain management (Walsh and Wellman 2000; Pan and Choi 2016), strategic simulation (Bunn and Oliveira 2003; Wang et al. 2016; Negahban and Smith 2018), operational risk and organizational networks (Frels et al. 2006), decision-making (Rai and Allada 2006;

<sup>1</sup> De facto, a dividend is defined as apart from net income, and the dividend payout decision is conditioned by the available cash flows, and not by net earnings. If one company records net earnings, but do not record a higher (or equal) amount of cash flows, as long as both dividends and financing investment projects require cash payments, the dividend policy is only a matter of (theoretical) accounting (Dragotă et al. 2019). In this study, we will consider a simplified case, respectively, profit ≡ cash flow. Signaling theories on dividends (e.g., Bhattacharya 1979; Kalay 1980) state that companies that pay dividends signal that they have sufficient cash for paying them, while non-payers can be suspected as not having it.

Dougherty et al. 2017; Zambrano and Olaya 2017), customers flows (Tan et al. 2008), algorithmic trading strategies (McGroarty et al. 2019), transport systems (Delcea et al. 2018a, 2018b; Monteiro et al. 2014), etc. The main advantage of agent-based models is their ability to take into account the heterogeneity of agents (McGroarty et al. 2019), meaning that each agent can have his or her own characteristics and can make decisions in accordance with them. Additionally, one agent can observe the environment and the actions/decisions made by other agents and he or she can decide which course of the action will be taken in the following. The adaptability and the responsiveness capacities of the agents make them actively observe and interact with the environment and with other agents. Moreover, the bounded-rationality property that the agents possess make them act as human beings while facing a decision situation, namely, they are just partially and not completely rational (Wilensky and Rand 2015). The abilities and the properties of the agents make the agent-based modelling proper to human-behavior modelling. Strictly related to the AGM situation, the agent-based modelling can be very useful for modelling the shareholders' behavior if we consider they do not share the same opinion in the context of a democratic vote. In this particular context, the agent-based modelling approach enables us to define several categories (named "classes" in our paper) to which the decision persons might belong to. Depending on one's class, several properties are enabled, which ensure that the agent is acting according to the assumed behavior of its class. From the interaction among the agents, different emerging behaviors and decisions can be observed and analyzed in depth.

Regarding the usage of the agent based modelling in the research area through the use of NetLogo platform, between 2003–2018, 512 papers using NetLogo have been published in ISI Web-of-Science in areas such as: operations research and management, business economics, finance, computer science, engineering, education and educational research, social sciences, mathematics, environmental sciences and ecology, etc. Thus, we have decided to use NetLogo for conducting the simulations as it offers a friendly user interface and an easy-to-write and understand syntax (Wilensky and Rand 2015). Among the characteristics of NetLogo, which differentiate it from other agent-based modelling software, one can underline that it is a free software, easy to use, and easy to understand even by persons outside the programming area, with a visual interface which allows one to see the changes in agents' properties in real-time, while the program is running, and which provides an extensive and up-to-date documentation.

The next section presents our model.

#### **3. The Model**

This section presents a model for the estimation of DSMBD. We structured this section in six sub-sections. The problem is defined in Section 3.1. We considered four classes of shareholders. Their behavior is described in Section 3.2. In Section 3.3 we discuss the evolution of the company's performance when making bad decisions persists. The agents' behavior is strongly influenced by the differences between the realized rate of return and their required rate of return. The manner of the estimation of the required rate of return is discussed in Section 3.4. We discuss the model inputs in Section 3.5. Section 3.6 presents the implementation in NetLogo. The decision-making process can be followed sequentially in Table 2.

We have chosen the agent-based modelling for representing, analyzing and simulation of this situation as in this type of modelling, each agent, representing a shareholder, can be endowed with a series of properties that makes it unique comparative to the other agents. Even more, as each different groups of shareholders have different expectations and points of view related to the company's efficient management, the inter-group properties of these agents are easy to model and represent. Even more, the NetLogo software offers a specific tool which enables one to conduct several experiments under different conditions and to observe, in real-time, the agents' behavior, as individuals and as a part of a particular group.


**2.**Theofthedecision-making

*JRFM* **2019** , *12*, 167


