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On the fault (in)tolerance of coordination mechanisms for distributed investment decisions

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Abstract

The efficient allocation of scarce financial resources lies at the core of financial management. Whenever humans are involved in the allocation process, it would be reasonable to consider abilities, in order to assure efficiency. For the context of coordinating investment decisions, the competitive hurdle rate (CHR) mechanism (Baldenius et al. in Account Rev 82(4):837–867, 2007) is well established for allocating resources. This mechanism is derived from an agency model, which, as is the nature of agency models, assumes agents as being fully competent. We employ the agentization approach (Guerrero and Axtell in Emergent results of artificial economics, Lect Notes Econ Math, vol 652. Springer, Berlin, pp 139–150, 2011) and transfer the logic behind the CHR mechanism into a simulation model, and account for individual incapabilities by adding errors in forecasting the initial cash outlay, the cash flow time series, and the departments’ ability to operate projects. We show that increasing the number of project proposals, and decreasing the investment alternatives diversity (in terms of their profitability only), significantly decreases the fault tolerance of our CHR mechanism. For misforecasting cash outlays, this finding is independent from the error’s dimension, while for larger errors in forecasting cash flows, and the departmental ability, the impact of diversity reverses. On the basis of our results, we provide decision support on how to increase the robustness of the CHR mechanism with respect to errors.

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Notes

  1. Agentization is the exercise of rendering neoclassical models into computational ones. In our case, it allows us to get rid of a neoclassical core assumption: agents are no more homogeneous [corresponding to the representative agent assumption, see, e.g., Kirman (1992)]. Moreover, we drop the assumption of agents being fully informed, presume that agents are not fully capable to execute their plans, and perceive agents to be able to interact (in spite of the fact that interaction is excluded between department managers, while the coordinating unit of a business organization interacts with all of its departments).

  2. Note that we do not refer to all investment opportunities within a cooperation, but only to those that compete for the same pot of funding.

  3. The simulation model was implemented using Visual Basic for Applications.

    Fig. 1
    figure 1

    Coordinating investment decisions: course of actions and information exchanged

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  4. In order to avoid negative forecasts, we limit the error terms to \(\pm 3\sigma \).

  5. Please notice that the introduced types of errors are independent of each other. I.e., to build up a simulator, we first generate the investment alternatives with all their associated measures upon realization. Then, second, the agents have to forecast these (unknown) measures whereby the forecasts are always based on the error-free case. Thus, errors are independent so that, for example, an erroneous forecast of the initial cash outlay (cf. Eq. 5) does not affect the basis for forecasting the cash flow time series, \(\chi _{it}\), in Eq. 6.

  6. Note that agency problems are excluded in the approach presented here. Thus, any deliberate misreporting from the departmental side can be ignored. Agents are ‘incompetent’ but not dishonest.

  7. Notice that changes \(H=[\underline{\eta },\overline{\eta }]\) affect each and every project.

  8. Please notice that Figs. 3, 4, and 5 plot the observed contours for \(T=3\) and \(n=6\). Very similar patterns can be observed for all investigated characterizations of the assets’ useful life, \(T\), and the number of departments, \(n\).

  9. Recall that project homogeneity only indicates that projects are similar in their returns on investment, ceteris paribus. Thus, if projects are more homogenous this does not imply that the money necessary to launch a project, the cash flow time series, and the departmental ability to operate a project are becoming more similar.

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Acknowledgments

In parts, the work of Doris A. Behrens was carried out within the SOSIE project, supported by Lakeside Labs GmbH, and partly funded by the European Regional Development Fund (ERDF) and the Carinthian Economic Promotion Fund (KWF) under grant no. 20214/23793/35529.

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Authors and Affiliations

  1. Department for Controlling and Strategic Management, Faculty of Management and Economics, Institute of Business Management, Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Stephan Leitner & Doris A. Behrens

  2. Department of Mathematical Methods in Economics, Research Unit for Operations Research and Control Systems, Vienna University of Technology, Vienna, Austria

    Doris A. Behrens

Authors
  1. Stephan Leitner
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Correspondence to Stephan Leitner.

Appendix

Appendix

See Tables 1, 2, 3, 4, 5, 6, 7, 8, and 9.

Table 1 Forecasting error cash flow time series, \(T=3\)
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Table 2 Forecasting error cash flow time series, \(T=5\)
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Table 3 Forecasting error cash flow time series, \(T=7\)
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Table 4 Forecasting error initial cash outlay, \(T=3\)
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Table 5 Forecasting error initial cash outlay, \(T=5\)
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Table 6 Forecasting error initial cash outlay, \(T=7\)
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Table 7 Forecasting error departmental ability, \(T=3\)
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Table 8 Forecasting error departmental ability, \(T=5\)
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Table 9 Forecasting error departmental ability, \(T=7\)
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Leitner, S., Behrens, D.A. On the fault (in)tolerance of coordination mechanisms for distributed investment decisions. Cent Eur J Oper Res 23, 251–278 (2015). https://doi.org/10.1007/s10100-013-0333-4

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