A Fractal Measure for Comparing the Work Effort of Human and Artificial Agents Performing Management Functions

Thanks to the growing sophistication of artificial agent technologies, businesses will increasingly face decisions of whether to have a human employee or artificial agent perform a particular function. This makes it desirable to have a common temporal measure for comparing the work effort that human beings and artificial agents can apply to a role. Existing temporal measures of work effort are formulated to apply either to human employees (e.g., FTE and billable hours) or computer-based systems (e.g., mean time to failure and availability) but not both. In this paper we propose a new temporal measure of work effort based on fractal dimension that applies equally to the work of human beings and artificial agents performing management functions. We then consider four potential cases to demonstrate the measure’s diagnostic value in assessing strengths (e.g., flexibility) and risks (e.g., switch costs) reflected by the temporal work dynamics of particular managers. I. THE NEED FOR A COMMON TEMPORAL MEASURE OF WORK EFFORT HE increasing power and sophistication of artificial agent technology is allowing businesses to employ artificial agents in a growing number of roles. Artificial agents are no longer restricted simply to performing logistical functions such as resource scheduling, but are now capable of more complex interpersonal workplace behavior such as using social intelligence to effectively manage the limitations, abilities, and expectations of human employees [1], recognizing and manifesting culture-specific behaviors in interactions with human colleagues [2], and assessing the performance of human members of virtual teams [3]. It is thus gradually becoming more feasible to design artificial agents capable of performing the four key functions carried out by human managers, which are planning, organizing, leading, and controlling [4]. T As a result of such recent and anticipated future advances, businesses will increasingly be faced with concrete decisions about whether, for example, the manager of a new corporate call center should be an experienced human manager or the latest artificial agent system. Such decisions will be shaped by a large number of strategic, financial, technological, political, legal, ethical, and operational factors. One particular element to be taken into account is that of temporal work effort: i.e., how much time would a human manager actually be able to dedicate to carrying out the necessary work functions, given the fact that physiological, cultural, legal, and ethical constraints limit the number of hours per week that a human being is capable of working? Similarly, how much time would an artificial agent be able to dedicate to carrying out the necessary work functions, given the fact that scheduled maintenance or unscheduled outages can limit the uptime of computer-based systems? Knowing how much time per day (or week, or other relevant time interval) a manager will be available to carry out his or her functions of planning, organizing, leading, and controlling becomes especially relevant in an interconnected age when global businesses operate around the clock, and managers are expected to be available to respond to inquiries and make decisions at almost any time of the night or day. In the case of human professionals, temporal measures such as ‘full-time equivalent’ (FTE) [5] and ‘billable hours’ are often used to quantify one’s work effort. Computerbased systems, meanwhile, often use temporal measures such as ‘availability’ and ‘reliability.’ In the following sections, we will analyze such existing measures and then develop a new fractal-dimension-based temporal measure for work effort that has at least two notable advantages: it is applicable to the work effort of both human and artificial agent managers, and it provides valuable diagnostic insights into the strengths and dangers of an individual manager’s temporal work dynamics that are not provided by existing measures. II.MEASURES OF WORK EFFORT FOR COMPUTER-BASED SYSTEMS A. Availability and Reliability A computer’s reliability is often quantified as the mean time to failure (MTTF), the average length of time that a system will remain continuously in operation before experiencing its next failure [6]. The mean time to repair (MTTR) is the average length of time needed to detect and repair the failure and return the system to operation. A computer’s steady-state availability A is the likelihood that the computer is operating at a particular moment, and is related to MTTF and MTTR in the equation [6]: