Johanna Seibt’s Process Ontology of Categorical Inference: On Nomological Axiomatics and Category Projection

Drawing from a Sellarsian realist-naturalist epistemology, we trace different levels of cognitive hierarchy procedures through which a representational system learns to update its own states and improve its ‘map-making’ capabilities from pre-conscious operations which modulate base-localization functions, to patterns of epistemic revision and integration at the conceptual and theoretical levels, producing a nomological double of its world. We show how ontological theorization becomes diachronically coordinated with and constrained by empirical science, and how the formal-quantitative kernel of scientific theories corresponds to qualitative-conceptual determinations at the structural level. Following Johanna Seibt’s characterization of ontology as a theory of categorial inference, we trace the preservation of inferential semantic structure across ontological theories in relation to model languages and provide provisional indications to coordinate Seibt’s account with a convergent realist assessment of systematic modeling, defining the epistemological conditions for articulating the preservation of formal structure in theories toward a limit-point of enquiry.

Risk perception, trust and support for wildlife reintroduction and conservation

Summary Wildlife reintroduction projects often face resistance from local residents who see potential conflicts with the species or lack trust or confidence in the agencies and professionals involved in reintroduction. Yet the linkages between trust, confidence, risk perceptions, attitudes towards the species and local support for its reintroduction are not well known. The Dual-Mode Model of Cooperation and Cognitive Hierarchy Model were theoretical frameworks used to shed light on these linkages by exploring the potential roles trust and confidence play as mediators between risk perceptions and attitudes towards, and support for, reintroduced elk in Tennessee (USA). A mail survey of 1005 residents living in the five-county area surrounding the North Cumberland Elk Restoration Zone assessed resident attitudes and risk perceptions towards the reintroduced elk, trust towards the managing wildlife agency and support for continued conservation efforts. A structural equation model revealed that trust and confidence play positive roles in mitigating risk perceptions and improving support for the reintroduction of elk. The findings confirm the roles public trust and confidence play in wildlife reintroductions and should help agencies work towards building local trust and confidence, minimizing risks, improving attitudes and increasing the chances for successful outcomes for the species and people.

Monte Carlo Tree Search and Cognitive Hierarchy Theory for Interactive-Behavior Prediction in Fast Trajectory Planning and Automated Lane Change

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communications and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. The main goal of this paper is to develop a fast algorithm that predicts the future states of the neighboring vehicles. The proposed workflow employs Monte Carlo Tree Search (MCTS) along with an on-policy learning technique for fast trajectory planning in multi-lane highway traffic scenarios. Also, for the inclusion of behavioral aspects, cognitive hierarchy and level-K game theories are utilized to predict the reaction and decision of the surrounding drivers. Simulation case studies demonstrate that our proposed approach is real-time implementable and can often avoid collision in difficult simulated confrontations.