Abstract
This chapter takes on the difficult task of relating the key aspects of rapidly evolving decision automation – along with the emerging techno-informational reality – to the goals and the mechanics of organizational sensemaking. Framed more by examples than by ontologically clear definition, the notion of decision automation is nonetheless rooted in some long-established principles, even though more than any other aspect of organizational learning, it is tied to technological change. With that in mind, the overview of this broad domain of knowledge and practice begins with a high-level discussion of the key automation manifestations, ranging from decision support systems to autonomous decision systems, followed by an overview of automation-enabling data analytic techniques, all set in the context of contrasting mini-case studies. The second part of this chapter takes a speculative, forward-looking view on how the combination of artificial and human intelligence will likely fuel the next evolutionary chapter of human creative development, all framed in the broad context of human-machine interactions.
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Notes
- 1.
The Defense Advanced Research Projects Agency, widely considered as the most prominent research organization of the US Department of Defense (many modern technologies, including the Internet, were born there)
- 2.
Human channel capacity is the idea originally proposed in 1956 by G A Miller, a psychologist, who placed a numeric limit of 7 ± 2 of discrete pieces of information an average human can hold in his/her working memory. According to the proponents of that notion, the reason the number of informational ‘chunks’ a person can simultaneously process in attention is finite, and fairly small, is due to limitations of short-term and working memory, which can be likened to physical storage restrictions of a computer system, or a physical container for that matter.
- 3.
A key, public-facing aspect of physical computer interface design is focused on application programming interface, or API design, which, firstly and foremost is manifestations of a digital platform’s (such as Expedia, eBay, or Salesforce) architecture and governance choices encompassing core design specifics such as partitioning, systems integration, decision rights, control, and pricing; secondly, API design is also an expression of the means by which digital platform providers create third-party developer and end customer ecosystems.
- 4.
This is a broad generalization as there are multiple distinct regression methodologies, including linear, logistic, and ordinal, all of which are mathematically distinct, and thus ultimately produce different result evaluation assessment-related outcomes; those differences notwithstanding, all those methods are analytically transparent.
- 5.
The test was called the ‘imitation game’ by Turing; it is conducted in a controlled setting with test subjects, a person and a computer program, being hidden from view of the judge whose job is to distinguish between answers given by the two subjects (it should be noted that the test does not check the ability to give correct answers – it is only concerned with how closely answers resemble those a human would give). If the judge cannot tell the difference between answers provided by the computer and the person, the computer has succeeded in demonstrating human intelligence.
- 6.
Most notably, Klumpp [2]
- 7.
In more operationally clear terms, that requires recoding of the original multicategory variable into as many dummy-coded (yes-no or 1-0) binary indicator variables as there are categories comprising the original variable.
- 8.
The general computational formula to be used here is n!/(r!(n − r)!), where n is the total number of possibilities and r is the number of selections; assuming just ten possibilities for the three-way interaction (occupation-gender-age category) used in the example would produce 1000 possible interactions.
- 9.
Portrait of Dr. Gachet sold in 1990 for $82.5 million, which in 2020 dollars would be about double that, or $161.4 million
References
Banasiewicz, A. (2013). Marketing database analytics: Transforming data for competitive advantage. New York: Routledge.
Klumpp, M. (2017). Automation and artificial intelligence in business logistics systems: Human reaction and collaborations requirements. Journal of Logistics Research and Applications, 21(3), 224–242.
Ogden, C. K., & Richards, I. A. (1927). Meaning of meaning. New York: Harcourt, Brace & Company.
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Banasiewicz, A. (2021). Decision Automation. In: Organizational Learning in the Age of Data. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-74866-1_7
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DOI: https://doi.org/10.1007/978-3-030-74866-1_7
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