**1. Introduction**

At least for some persons, it seems very easy to define a decision as being good or bad. For instance, a religious, racist, or nationalistic individual can have no doubt that all he or she does in the name of his or her faith is a good decision, and what is contrary to the accepted dogma is a bad decision. On the other hand, other persons would consider, without any doubt, that the decisions made in the name of the same values are bad decisions, which affect the life and its quality of many innocent people (e.g., children, victims of different kinds of discrimination), science, education, etc. Finally, all the parties can produce different arguments for supporting their values and can refuse the others' arguments. In other cases, even the negative effects can be identified by everyone, the parties involved in making decisions support one decision as being the only one acceptable. For instance, for some persons it is difficult to support measures for protecting the environment and reduce emissions, since the GDP/capita is too small and not enough railways and factories are present in the region. For others, sustainable development is a necessity (e.g., Lele 1991; Doyle and Stiglitz 2014; Armeanu et al. 2018). Unfortunately, the incapacity to provide a unanimous final answer for the best decision, and if one decision is definitely bad (at least for some people) it does not mean that the problem is not important. Even more, even if consensus between parties is not achieved, it does not mean that the problem does not exist.

For some reasons, finance can be a safer place for defining a bad decision. One main advantage is that finance has a clear ideology (e.g., the search for maximizing the shareholders' wealth) (Ross et al. 2010; Belghitar et al. 2019), and also clear instruments for monitoring the deviations from it. If one decision determines a loss (e.g., a decrease in shareholders' wealth), it is a bad decision. Additionally, if a decision determines a lower return than another, it can be reasonably considered that selecting this one was worse. Probably for this reason the presence of making bad decisions is well documented in economics and finance in different contexts (e.g., De Bondt and Thaler 1995; Rubinstein 2001; Ariely 2009; Campbell et al. 2009; Taleb et al. 2009; Gennaioli et al. 2015, etc.). However, all these conclusions can be formulated only after these decisions produce their outcomes (ex post) (Campbell et al. 2009). Initially, most of them are decisions in a risk-context, which can be better or worse than the others. For instance, making decisions based on the assumed knowledge at one moment, using the classical model, based on Gaussian distribution for modelling return distribution and, thus, neglecting the extreme values, determined losses which were unexpected initially (Taleb 2007; Taleb et al. 2009). It was a bad decision to use the Gaussian distribution but, for the most part, the deciders were convinced that using the Gaussian distribution for modelling returns is a good, normal, one.

In some cases, these decisions can become systematic. Human history provides many examples of making systematically bad decisions, sometimes after a long time of making good decisions (Gilbert 2011; Lucero et al. 2011; Harari 2015). In this paper, we analyze the duration of systematically making bad decisions, defined as the length in time of making erroneous mistakes, based on the same mental algorithm. The end of making bad decisions can be decided by other parties; from this perspective, the end of making bad decisions can be the end of the decider's position.

In corporate finance theory, based on the principle of maximizing the shareholders' wealth (Ross et al. 2010; Belghitar et al. 2019), the impact of the existence of one decider that make systematic bad decisions is only marginally considered. However, in some cases, the quality of different decisions can be disputable (Morgan and Hansen 2006). Moreover, this issue becomes more complicated if multi-objective optimization is considered (Lovric et al. 2010). In the same line, different cultural (not financial) values can have an impact on financial decisions (Fidrmuc and Jacob 2010; Ucar 2016). These different values can result in contrary decisions, which can be considered "good" or "bad", depending on each culture's perspective.

In certain cases, some agents can make good decisions, but they have to accept the viewpoint imposed by some other agents, which make bad decisions. For instance, at the corporate level, dividend policy is decided by vote, democratically. Some bad decisions made by the dominating group of voters can affect the rational shareholders' wealth. People are different, thus, different features (e.g., overconfidence, pattern recognition, etc.) of some agents can affect the wealth of other shareholders (Campbell et al. 2009). Bad decisions, but, even more, systematic bad decisions, can affect the investors' quality of life in the long-term (and sometimes, probably irreversible; for instance, in the case of retired employees, regarding their pension plans).

Shareholders' wealth can be affected by dividend policy. Dividend policy is discussed in many papers and is approached from different perspectives (Graham and Dodd 1951; Lintner 1964; Miller and Modigliani 1961; Miller and Scholes 1978; Easterbrook 1984; La Porta et al. 2000a, 2000b; Fidrmuc and Jacob 2010; Shao et al. 2010; Ucar 2016; Jiang et al. 2017, etc.). The discussions regarding an optimal dividend policy still continue. Contrary viewpoints can be considered good or bad, depending on each side's perspective. Thus, the dividend payout decision is a possible fruitful field for analyzing the duration of systematically making bad decisions.

In this paper, we propose a model in which shareholders are not aware instantly about a bad decision made by the shareholders that dominate annual general meetings of shareholders (hereafter, AGM; in Table 1 are provided all the abbreviations and notations). In our paper, we consider a non-homogenous behavior of the shareholders implied in setting a dividend policy at an AGM (and supporting or not the power) through agent-based models (Zambrano and Olaya 2017; Negahban and Smith 2018; McGroarty et al. 2019, etc.). Especially, due to the diversity of the conclusions

regarding dividend policy, this is a good field for analysis of the impact of systematically making bad decisions. It is very difficult to state a priori that a decision is bad or not, because each dividend policy can be considered right for some reasons, and wrong for others. For this reason, considering a non-homogenous behavior for the shareholders can be a contribution to the existent literature. Some studies consider, in other contexts, different classes of shareholders, for instance, controlling shareholders versus minority shareholders (La Porta et al. 2000a), each one having specific interests. In our paper, we consider a more general case, in which shareholders can follow the same objective, but having different opinions about the manner of action, or having different perceptions about the company's perspectives. Moreover, we consider that the option of different shareholders for supporting one of another policy can be reversible in time.

Bad decisions have an impact on the company's performance. However, their impact is not instant. One issue that complicate even more the problem in some cases is the long period between the moment of making the decision and the moment when the outputs can be checked (see Figure 1). A bad decision is difficult to be identified in earliest stages, but only after a long period. In general, decisions regarding dividend payout (*DPRt*) are made considering exclusively expected levels for indicators, for example comparing the expected internal rate of return of the proposed investment project (*Et*(*IRRt*+1)) with the expected required rate of return (*Et*(*kD*,*t*<sup>+</sup>1)).

In this paper, we propose a model, which can be used in simulations, regarding the impact of systematically making bad decisions. We use this model for the estimation of the *duration of systematically making bad decisions* (hereafter, DSMBD). Some of the issues considered in our study can be found in Dragotă (2016). However, comparative to Dragotă (2016), this study proposes an application for financial management. This application concerns the dividend policy.

The decisions (for example, using net present values (NPV) or internal rate of return (IRR) criterions) are made the moment 0, based on some considerations, including the expected level of cash flows generated in the future (at the moment t). In this figure, we consider two classes of agents—A and B, which expect at the present moment (0), future levels (at moment t) for the cash flow determined by the project—*EA0(CFt)*, *EB0(CFt)*. Class B makes good predictions for these cash flows. The class of agents make a bad prediction (biased, modelled by us through a coefficient *Π*, with *Π* ∈(0,1)) which conducts to wrong decisions. Agents from class A will be convinced that their decision was bad only at the moment n, comparing the realized level of cash flow (*CFt*) with the anticipated level for this cash flow—*EA0(CFt)*. Thus, only after some financial exercises, the decisions can be validated as good or wrong.

**Figure 1.** The long period between the decision-making process and the control of its result.





We propose an agent-based model for the estimation of DSMBD. We use NetLogo 6.0.4 (https: //ccl.northwestern.edu/netlogo/), which offers an easy-to-understand programming language and a graphical interface, where the changes in the simulated environment can be observed in real-time.

In the proposed model, we consider four classes of shareholders, each of them with a specific behavior (Dragotă 2016). We propose an algorithm that can be used in modelling their interaction and for predicting DSMBD, considering variables used in the practice of making decisions regarding dividend policy. Thus, the changes in voting structure can be followed in real-time.

We perform some simulations based on the proposed model. We prove that, as a result of agents' interaction, in some conditions, DSMBD can be very long. Thus, some numerical simulations suggest that, in some circumstances, this duration can significantly exceed the human lifetime. Additionally, in some conditions, the company can fail before the power is switched. DSMBD can increase dramatically if the shareholders have a great level of trust in the management's decisions. The democratic voting process and the good intentions are not sufficient conditions for making good decisions. As an implication, a greater concern for the quality of financial education, and more performant instruments for controlling the power's decisions, are required.

Considering its implications, this paper can be useful both for academics and for practitioners. A better understanding of the process of systematically making bad decisions can be beneficial for the academic literature. Highlighting the determining factors that can determine an increase of DSMBD and performing simulations for finding their impact can be a contribution in understanding a less studied process. As far as we know, this approach is new in analyzing dividend policy. For practitioners (e.g., investors in capital markets, other shareholders), it provides a decisional tool for anticipating some possible problems in the decision-making process, for a better control at the corporations' level. A systematic bad decision can affect the investor's wealth, with potentially disastrous effects in the long-term. For instance, a person can find that his or her pension funds are negatively affected when it is too late to make adjustments in portfolios.

The remainder of this paper is organized as follows. The next section presents the theoretical background. For our purpose, we have proposed a model for the estimation of DSMBD. Section 3 provides the model design and describes its implementation in NetLogo 6.0.4. Some numerical results are presented and discussed in Section 4. Section 5 concludes this paper.
