title: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

authors: Death, Russell G.

abstract: Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

text: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

year: 2017

title: Ideology is theft: Thoughts on the legitimacy of a Maori psychology

authors: Tamatea, Armon J.

subjects: Maori

abstract: ‘War, in fact, can be seen as a process of achieving equilibrium among unequal technologies’ (McLuhan, 1964) 
We are at war. As Western science and its accompanying technology expands the frontiers of knowledge at an ever-increasing rate, ‘indigenous’ perspectives of knowledge are exiled into the borderlands of special interest groups and localized research programmes. Mainstream scientific thought lays claim to objective interpretations of experience at the expense of alternative realities offered by emerging theories of knowledge. Furthermore, as localized worldviews (i.e., those derived from ancestral knowledge bases and pre-industrial or non-scientific premises) challenge existing paradigms, the inevitable interactions threaten to undermine the fidelity of this knowledge. One such arena where this ideological conflict is apparent is the growing field of Maori psychology.

text: Ideology is theft: Thoughts on the legitimacy of a Maori psychology ‘War, in fact, can be seen as a process of achieving equilibrium among unequal technologies’ (McLuhan, 1964) 
We are at war. As Western science and its accompanying technology expands the frontiers of knowledge at an ever-increasing rate, ‘indigenous’ perspectives of knowledge are exiled into the borderlands of special interest groups and localized research programmes. Mainstream scientific thought lays claim to objective interpretations of experience at the expense of alternative realities offered by emerging theories of knowledge. Furthermore, as localized worldviews (i.e., those derived from ancestral knowledge bases and pre-industrial or non-scientific premises) challenge existing paradigms, the inevitable interactions threaten to undermine the fidelity of this knowledge. One such arena where this ideological conflict is apparent is the growing field of Maori psychology.

year: 2008

title: Five abstraction rules to remove transitions while preserving compositional synthesis results

authors: Mohajerani, Sahar

subjects: computer science

abstract: This working paper investigates under which conditions transitions can be removed from an automaton while preserving important synthesis properties. The work is part of a framework for compositional synthesis of least restrictive controllable and nonblocking supervisors for modular discrete event systems. The method for transition removal complements previous results, which are largely focused on state merging. Issues concerning transition removal in synthesis are discussed, and redirection maps are introduced to enable a supervisor to process an event, even though the corresponding transition is no longer present in the model. Based on the results, different techniques are proposed to remove controllable and uncontrollable transitions, and an example shows the potential of the method for practical problems.

text: Five abstraction rules to remove transitions while preserving compositional synthesis results This working paper investigates under which conditions transitions can be removed from an automaton while preserving important synthesis properties. The work is part of a framework for compositional synthesis of least restrictive controllable and nonblocking supervisors for modular discrete event systems. The method for transition removal complements previous results, which are largely focused on state merging. Issues concerning transition removal in synthesis are discussed, and redirection maps are introduced to enable a supervisor to process an event, even though the corresponding transition is no longer present in the model. Based on the results, different techniques are proposed to remove controllable and uncontrollable transitions, and an example shows the potential of the method for practical problems.

year: 2012-03