Machine Actors Research Articles

Transforming the residential built environment: Land-use policies and the influence of the growth machine across the United States

What effects do growth machine actors have on transformation of the built environment in terms of localities' enacted land-use policies and actual development? To the best of our knowledge, this study is among the first to answer this question for communities across the United States. We draw on the growth machine theory and speak to some of its proponents’ puzzling findings that policies designed to limit growth often facilitate it. Our analysis is grounded in the urban-quantitative tradition of large-sample studies of U.S. localities. We find that involvement of growth machine business actors in local government is related to transformation of the built environment as indicated by the issuance of new residential building permits. Intriguingly, involvement of local growth actors is also associated with greater use of land-use control policies, which, in turn, have positive rather than negative effects on issuance of new residential building permits. Thus, it seems growth machine actors support policies that outwardly appear to stymie growth, although they in practice do not. These findings suggest land-use policies are often designed with enough leeway for continued growth. Supporting such policies allows growth machine actors to publicly signal support for managed growth and environmental protection, although the reality on the ground remains business as usual.

Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems.

From Analog to Algorithm

In the digital age, the current Management literature remains heavily rooted in anthropocentric and analog perspectives, often overlooking the profound influence of algorithms and artificial intelligence (AI) in modern organizational dynamics. To this end, this systematic literature review seeks to bridge this gap by exploring the evolving landscape of AI in organizations and the role of the Actor-Network Theory (ANT) as a theoretical-methodological lens for understanding AI’s impact on Management. Through a systematic literature review, we address research on the utilization of AI in Management over the past decade, the applications of ANT in non-IT or non-AI management studies, and studies explaining IT or AI concepts via ANT. Our findings highlight a significant research gap in understanding human-machine interactions and Business Management within organizations that use AI. Additionally, we identify potential avenues for future scholarly contributions, emphasizing the need for a more integrated approach that considers human and machine actors in organizational contexts.

Construal level theory in the design of informational systems

In the context of human-machine teaming, we are observing new kinds of automated and “intelligent” applications that effectively model and manage both producer and consumer aspects of information presentation. Information produced by the application can be easily accessed by the user at multiple levels of abstraction, depending on the user’s current context and necessity. The research described in this article applies this concept of information abstraction to complex command and control systems in which distributed autonomous systems are managed by multiple human teams. We explore three multidisciplinary and foundational concepts that can be used to design information flow in human-machine teaming situations: 1) formalizing a language we call “RECITAL” (Rules of Engagement, Commander’s Intent, and Transfer of Authority Language), which defines the information flow based on concepts of intent, rules, and delegated authority; 2) applying this language to well-established models of human-machine distributed teams represented as a systemic control hierarchy; and 3) applying construal level theory from social psychology as a means to guide the producer-consumer model of the information abstractions. All three of these are integrated into a novel user interface concept designed to make information available to both human and machine actors based on task-oriented decision criteria. In this research, we describe a conceptual model for future information design to inform shared control and decision-making across distributed human and machine teams. We describe the theoretical components of the concepts and present the conceptual approach to designing such systems. Using the concept, we describe a prototype user interface to situationally manage the information in a mission application.

Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems.

Human-Robot Teaming Configurations: A Study of Interpersonal Communication Perceptions and Affective Learning in Higher Education

Technology encourages collaboration in creative ways in the classroom. Specifically, social robots may offer new opportunities for greater innovation in teaching. In this study, we combined the established literature on co-teaching teams with the developing field of machine actors used in education to investigate the impressions students had of different team configurations that included both a human and a robot. Participants (N = 215, age: M = 24, SD = 8.67, range 18–69) saw one of three teams composed of a human and a social robot with different responsibilities present a short, prerecorded lecture (i.e., human as lead teacher-robot as teaching assistant, robot as lead teacher-human as teaching assistant, human and robot as co-teachers). Overall, students rated the human-led team as more appealing and having more credibility than the robot-led team. The data suggest that participants would be more likely to take a course led by a human instructor than a social robot. Previous studies have investigated machine actors in the classroom, but the current findings are unique in that they compare the individual roles and power structures of human-robot teams leading a course.

Decline machines and economic development: rust belt cities and Flint, Michigan

ABSTRACT We develop the concept of decline machines, only recently invoked by urbanists, to understand city economic development in America’s urban rust belt. We chronicle this notion and show that it currently spearheads economic development in one of America’s paradigmatic rust belt cities, Flint, Michigan. We highlight, first, that Flint’s machine actors creatively use imaginaries of decline to propel economic development; and second, that race is one core dimension through which this process proceeds. Coalition actors use dominant understandings of poor blackness and Latinoness – as people both primitivist exotic and frightening – to help drive economic development. The underpinning: Â austerity and neoliberal days impose profound constraints on key coalition actors, notably local planners, and local government, to create wider-ranging hunts for political resources like decline. Using this decline, economic development in Flint is shown to be more complicated in its staging, implementing, and outcomes than previously believed.

Systematic literature review on intent‐driven systems

An intent-driven system is a compositional system of human actors and machine actors. The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of the intents. Only then are different machine actors able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial collaboration. The aim is to find existing methods/techniques which could be used as building blocks to construct intent-driven systems. This is used to provide insight into what is needed to enable intent-driven systems with the help of these methods/techniques. As a part of a design science study, a Systematic Literature Review is conducted. The existences of methods/techniques which can be used as building blocks to construct intent-driven systems exist in the literature. How these methods/techniques can interact in order to enable realisations of intent-driven systems is not evident in the existing literature. The synthesis shows a need for further research regarding the semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.

A Balanced Perspective on Prediction and Inference for Data Science in Industry

The strategic role of data science teams in industry is fundamentally to help businesses to make smarter decisions. This includes decisions on minuscule scales, such as what fraction of a cent to bid on an ad placement displayed in a web browser, whose importance is only manifest when scaled by orders of magnitude through machine automation. But it also extends to singular, monumental decisions made by businesses, such as how to position a new entrant within a competitive market. In both regimes, the potential impact of data science is only realized when both humans and machine actors are learning from data and when data scientists communicate effectively to decision makers throughout the business. I examine this dynamic through the instructive lens of the duality between inference and prediction. I define these concepts, which have varied use across many fields, in practical terms for the industrial data scientist. Through a series of descriptions, illustrations, contrasting concepts, and examples from the entertainment industry (box office prediction and advertising attribution), I offer perspectives on how the concepts of inference and prediction manifest in the business setting. From a balanced perspective, prediction and inference are integral components of the process by which models are compared to data. However, through a textual analysis of research abstracts from the literature, I demonstrate that an imbalanced, prediction-oriented perspective prevails in industry and has likewise become increasingly dominant among quantitative academic disciplines. I argue that, despite these trends, data scientists in industry must not overlook the valuable, generalizable insights that can be extracted through statistical inference. I conclude by exploring the implications of this strategic choice for how data science teams are integrated in businesses.

Moral Orthoses: A New Approach to Human and Machine Ethics

I argue that both human and machine actions are more opaque than is generally realized and that the actions of both require explanation that an ethical orthosis might provide as aspects of artificial Companions for both human and machine actors. These explanations might well be closer to ethical accounts based on moral sentiment or emotion in the tradition of the primacy of sentiment over reason in this area of human and machine action.

Entrepreneurial Social Infrastructure and Growth Machine Characteristics Associated with Industrial-Recruitment and Self-Development Strategies in Nonmetropolitan Communities

Elements of entrepreneurial social infrastructure and the growth machine perspective are used to contrast industrial recruitment and self-development activities—two different types of community economic development. Data come from a purposive survey of 287 key informants associated with successful economic development projects in nonmetropolitan places throughout the U.S. The pattern of resource mobilization, and individual and organizational support for or opposition to both types of projects, is analyzed. Self-development projects are found to have access to more diverse resources and engage a greater diversity of local actors, while industrial recruitment is found to frequently engage local growth machine actors. Industrial recruitment is also found to rely heavily on resources and support from local and state governments. Opposition to projects, regardless of type, occurred in about one-third of the projects and was generally associated with occupations anticipated to be anti-growth. These findings have several implications for development professionals, including increased understanding of how to improve a community's overall capacity for engaging in economic development.