scholarly journals Expectations affect physical causation judgments

2019 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Thomas Icard
Keyword(s):  
Causal Reasoning ◽  
Social Reasoning ◽  
Physical Systems ◽  
Important Challenge ◽  
Normative Expectations ◽  
Physical Interactions ◽  
Per Se ◽  
Intentional Agents ◽  
Physical Causation ◽  
Causal Cognition

When several causes contributed to an outcome, people often single out one as "the" cause. What explains this selection? Previous work has argued that people select abnormal events as causes, though recent work has shown that sometimes normal events are preferred over abnormal ones. Existing studies have relied on vignettes that commonly feature agents committing immoral acts. An important challenge to the thesis that norms permeate causal reasoning is that people's responses may merely reflect pragmatic or social reasoning rather than arising from causal cognition per se. We tested this hypothesis by asking whether the previously observed patterns of causal selection emerge in tasks that recruit participants' causal reasoning about physical systems. Strikingly, we found that the same patterns observed in vignette studies with intentional agents arise in visual animations of physical interactions. Our results demonstrate how deeply normative expectations affect causal cognition.

scholarly journals Changing Minds ― Epistemic Interventions in Causal Reasoning

2021 ◽  
Author(s):  
Lara Kirfel ◽  
David Lagnado
Keyword(s):  
Allergic Reaction ◽  
Causal Reasoning ◽  
Epistemic State ◽  
Counterfactual Reasoning ◽  
Epistemic States ◽  
Epistemic Actions ◽  
Causal Action ◽  
Epistemic Context ◽  
Future Outcomes ◽  
Causal Cognition

Did Tom’s use of nuts in the dish cause Billy’s allergic reaction? According to counterfactual theories of causation, an agent is judged a cause to the extent that their action made a difference to the outcome (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2020; Gerstenberg, Halpern, & Tenenbaum, 2015; Halpern, 2016; Hitchcock & Knobe, 2009). In this paper, we argue for the integration of epistemic states into current counterfactual accounts of causation. In the case of ignorant causal agents, we demonstrate that people’s counterfactual reasoning primarily targets the agent’s epistemic state – what the agent doesn’t know –, and their epistemic actions – what they could have done to know – rather than the agent’s actual causal action. In four experiments, we show that people’s causal judgment as well as their reasoning about alternatives is sensitive to the epistemic conditions of a causal agent: Knowledge vs. ignorance (Experiment 1), self-caused vs. externally caused ignorance (Experiment 2), the number of epistemic actions (Experiment 3), and the epistemic context (Experiment 4). We see two advantages in integrating epistemic states into causal models and counterfactual frameworks. First, assuming the intervention on indirect, epistemic causes might allow us to explain why people attribute decreased causality to ignorant vs. knowing causal agents. Moreover, causal agents’ epistemic states pick out those factors that can be controlled or manipulated in order to achieve desirable future outcomes, reflecting the forward-looking dimension of causality. We discuss our findings in the broader context of moral and causal cognition.

Cyber-Physical Systems

2017 ◽  
pp. 335-357 ◽  
Author(s):  
Lydia Ray
Keyword(s):  
Embedded Systems ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Transportation Energy ◽  
Physical Systems ◽  
Significant Research ◽  
Important Challenge ◽  
Recent Trends ◽  
Human Brains ◽  
Privacy Issues

Pervasive computing has progressed significantly with a growth of embedded systems as a result of recent advances in digital electronics, wireless networking, sensors and RFID technology. These embedded systems are capable of producing enormous amount of data that cannot be handled by human brains. At the same time, there is a growing need for integrating these embedded devices into physical environment in order to achieve a far better capability, scalability, resiliency, safety, security and usability in important sectors such as healthcare, manufacturing, transportation, energy, agriculture, architecture and many more. The confluence of all these recent trends is the vision of distributed cyber-physical systems that will far exceed the performance of traditional embedded systems. Cyber-physical systems are emerging technology that require significant research in design and implementation with a few important challenges to overcome. The goal of this chapter is to present an overview of basic design and architecture of a cyber-physical system along with some specific applications and a brief description of the design process for developers. This chapter also presents a brief discussion of security and privacy issues, the most important challenge of cyber-physical systems.

scholarly journals The Psychology of Causal Perception and Reasoning

2010 ◽  
Author(s):  
David Danks
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Psychological Research ◽  
Causal Learning ◽  
Causal Beliefs ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Causal Perception ◽  
The World ◽  
Causal Cognition

Causal beliefs and reasoning are deeply embedded in many parts of our cognition. We are clearly ‘causal cognizers’, as we easily and automatically (try to) learn the causal structure of the world, use causal knowledge to make decisions and predictions, generate explanations using our beliefs about the causal structure of the world, and use causal knowledge in many other ways. Because causal cognition is so ubiquitous, psychological research into it is itself an enormous topic, and literally hundreds of people have devoted entire careers to the study of it. Causal cognition can be divided into two rough categories: causal learning and causal reasoning. The former encompasses the processes by which we learn about causal relations in the world at both the type and token levels; the latter refers to the ways in which we use those causal beliefs to make further inferences, decisions, predictions, and so on.

Cyber-Physical Systems

2020 ◽  
pp. 128-150 ◽  
Author(s):  
Lydia Ray
Keyword(s):  
Embedded Systems ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Transportation Energy ◽  
Physical Systems ◽  
Significant Research ◽  
Important Challenge ◽  
Recent Trends ◽  
Human Brains ◽  
Privacy Issues

Pervasive computing has progressed significantly with a growth of embedded systems as a result of recent advances in digital electronics, wireless networking, sensors and RFID technology. These embedded systems are capable of producing enormous amount of data that cannot be handled by human brains. At the same time, there is a growing need for integrating these embedded devices into physical environment in order to achieve a far better capability, scalability, resiliency, safety, security and usability in important sectors such as healthcare, manufacturing, transportation, energy, agriculture, architecture and many more. The confluence of all these recent trends is the vision of distributed cyber-physical systems that will far exceed the performance of traditional embedded systems. Cyber-physical systems are emerging technology that require significant research in design and implementation with a few important challenges to overcome. The goal of this chapter is to present an overview of basic design and architecture of a cyber-physical system along with some specific applications and a brief description of the design process for developers. This chapter also presents a brief discussion of security and privacy issues, the most important challenge of cyber-physical systems.

scholarly journals Causal Reasoning and Event Cognition as Evolutionary Determinants of Language Structure

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 843
Author(s):  
Peter Gärdenfors
Keyword(s):  
Causal Reasoning ◽  
Noun Phrases ◽  
Mental Event ◽  
Event Representation ◽  
Event Cognition ◽  
Verb Phrases ◽  
External Forces ◽  
Event Representations ◽  
Insight Into ◽  
Causal Cognition

The aim of this article is to provide an evolutionarily grounded explanation of central aspects of the structure of language. It begins with an account of the evolution of human causal reasoning. A comparison between humans and non-human primates suggests that human causal cognition is based on reasoning about the underlying forces that are involved in events, while other primates hardly understand external forces. This is illustrated by an analysis of the causal cognition required for early hominin tool use. Second, the thinking concerning forces in causation is used to motivate a model of human event cognition. A mental representation of an event contains two vectors representing a cause as well as a result but also entities such as agents, patients, instruments and locations. The fundamental connection between event representations and language is that declarative sentences express events (or states). The event structure also explains why sentences are constituted of noun phrases and verb phrases. Finally, the components of the event representation show up in language, where causes and effects are expressed by verbs, agents and patients by nouns (modified by adjectives), locations by prepositions, etc. Thus, the evolution of the complexity of mental event representations also provides insight into the evolution of the structure of language.

Why Presence Occurs: Evolutionary Psychology, Media Equation, and Presence

2004 ◽  
Vol 13 (4) ◽  
pp. 494-505 ◽  
Author(s):  
Kwan Min Lee
Keyword(s):  
Social Presence ◽  
Causal Reasoning ◽  
Folk Psychology ◽  
Computer Users ◽  
Human Beings ◽  
Media Equation ◽  
Folk Physics ◽  
Fundamental Reason ◽  
The Media ◽  
Physical Causation

Despite the intense interest in the phenomena of presence, there have been limited attempts to explain the fundamental reason why human beings can feel presence when they use media and/or simulation technologies. This is mainly because previous studies on presence have focused on “what” questions—what are the causes and effects of presence?—rather than the “why” question. The current paper tries to solve this problem by providing an elaborated—and probably controversial—account of the fundamental presence-enabling mechanism. More specifically, it explains the modularity of human minds, and proposes that human beings can feel presence due to the automatic application of two types of causal reasoning modules—folk-physics modules for knowing about physical causation, and folk-psychology modules for knowing about social causation—when they respond to mediated and/or simulated objects. Finally, it explains the media-equation phenomena (in which media or computer users feel physical or social presence) according to the modularity argument.

scholarly journals The relevance of causation in robotics: A review, categorization, and analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 238-255
Author(s):  
Thomas Hellström
Keyword(s):  
Research Methodology ◽  
Causal Reasoning ◽  
Machine Ethics ◽  
Research Areas ◽  
Problem Formulations ◽  
Published Research ◽  
And Robotics ◽  
Causal Cognition

Abstract In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlier work in robotics and also connect to research in other sciences. While the proposed categories mainly cover the sense–plan–act level of robotics, we also identify a number of higher-level aspects and areas of robotics research where causation plays an important role, for example, understandability, machine ethics, and robotics research methodology. Overall, we conclude that causation underlies several problem formulations in robotics, but it is still surprisingly absent in published research, in particular when it comes to explicit mentioning and using of causal concepts and terms. We discuss the reasons for, and consequences of, this and hope that this article clarifies the broad and deep connections between causal reasoning and robotics and also by pointing at the close connections to other research areas. At best, this will also contribute to a “causal revolution” in robotics.

scholarly journals A counterfactual simulation model of causation by omission

2020 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Simon Stephan
Keyword(s):  
Simulation Model ◽  
Causal Explanation ◽  
Physical Situation ◽  
Prior Work ◽  
Causal Responsibility ◽  
Causal Judgments ◽  
Relevant Variables ◽  
Normative Expectations ◽  
Physical Interactions ◽  
Counterfactual Simulation

When do people say that an event that didn't happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of sub-problems that need to be addressed in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions by mentally simulating what would have happened in relevant counterfactual situations. People use their intuitive understanding of physics to run these mental simulations. While prior work has argued that normative expectations affect judgments of omissive causation, we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.

scholarly journals Agent based coordination protocol for system of cyber-physical systems

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Aisha Umair ◽  
Anders Clausen ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Communication Technology ◽  
Cyber Physical Systems ◽  
Coordination Mechanism ◽  
Virtual Power ◽  
Agent Based ◽  
Physical Systems ◽  
Important Challenge ◽  
Information And Communication ◽  
Greenhouse Growers ◽  
The Individual

AbstractNowadays, society and business rely heavily on Information and Communication Technology (ICT) systems, which are progressing faster than ever. To stay on pace with them, focus is shifted towards integration of individual ICT systems into complex systems, which offers more functionality than simply the sum of individual systems. In this regard, Cyber-Physical Systems (CPSs) have gained significant importance and System-of-Systems (SoS) approach has been suggested for modeling complex CPSs to achieve a higher level goal, by dynamically building a large system with existing autonomous, and heterogeneous constituent systems (CSs). An important challenge in a system of Cyber-Physical Systems (SoCPSs) is to develop seamless collaboration between autonomous constituent-CPSs (CCPSs) to coordinate their operations. In this paper, we propose an agent based coordination mechanism to coordinate resource allocation and demand in SoCPSs. The approach models each CCPS as an agent and describes how multiple autonomous CCPSs, i.e., Virtual Power Plant (VPP), Commercial Greenhouse Growers (CGGs), communicate and collaborate with each other asynchronously through negotiation and how potential conflicts between CCPSs with conflicting goals are resolved. The efficacy of the proposed mechanism is validated through simulation of different real-world acyclic SoCPSs topologies. The results show that proposed approach is able to balance the individual requirements of multiple connected CPSs while achieving SoCPSs’ mission.

scholarly journals Towards ending the animal cognition war: a three-dimensional model of causal cognition

2021 ◽  
Vol 36 (2) ◽  
Author(s):  
Tobias Benjamin Starzak ◽  
Russell David Gray
Keyword(s):  
Cognitive Abilities ◽  
Causal Reasoning ◽  
Animal Cognition ◽  
Three Dimensional ◽  
Future Research ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Causal Understanding ◽  
Similarities And Differences ◽  
Causal Cognition

AbstractDebates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy.

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