Strategies for water pollution control in Dhaka, Bangladesh: an institutional and economic analysis

This paper presents primary data based on research carried out as part of a large World Bank project. Results from our survey show that water pollution in Dhaka watershed has reached alarming levels and is posing significant threats to health and economic activity, particularly among the poor and vulnerable. Rice productivity in the watershed area, for example, has declined by 40% in recent years and vegetable cultivation in the riverbeds has been severely damaged. We also found significant correlation between water pollution and diseases such as jaundice, diarrhoea and skin problems. It was reported that the cost of treatment of skin diseases for one episode could be as high as 29% of the weekly earnings of poor households. Given the magnitude of the contamination problem, a multi-agent stakeholder approach was necessary to analyse the institutional and economic constraints that would need to be addressed in order to improve environmental management. This approach, in turn, enabled core strategies to be developed. The strategies were better understood around three types of actors in industrial pollution, i.e. (1) principal actors, who contribute directly to industrial pollution; (2) stakeholders, who exacerbate the situation by inaction; and (3) the potential actors in mitigation of water contamination. Within a carrot-and-stick framework, nine strategies leading to the strengthening of environmental management were explored. They aim at improving governance and transparency within public agencies and private industry through the setting up of incentive structures to advance compliance and enforcement of environmental standards. Civil society and the population at large are, on the other hand, encouraged to contribute actively to the mitigation of water pollution by improving the management of environmental information and by raising public awareness.

Distributed belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-research topic within AI. We argue that the new model of distributed belief revision as discussed here is suitable for general modelling of judicial decision making, along with extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interaction with, and influencing, other agents who are deliberating collectively. In the approach proposed, it's the entire group of agents, not an external supervisor, who integrate the different opinions. This is achieved through an election mechanism, The principle of "priority to the incoming information" as known from AI models of belief revision are problematic, when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stiumuli) could attempt to handle other aspects of the deliberation which are more specifi to legal narrative, to argumentation in court, and then to the debate among the jurors.

Belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-researched topic within Artificial Intelligence (AI). We argue that the new model of belief revision as discussed here is suitable for general modelling of judicial decision making, along with the extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interacting with, and influencing, other agents who are deliberating collectively. The principle of 'priority to the incoming information', as known from AI models of belief revision, is problematic when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet, we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stimuli) could attempt to handle other aspects of the deliberation which are more specific to legal narratives, to argumentation in court, and then to the debate among the jurors.