Mismatched Ontologies by Ramesh Srinivasan
Excerpts from the original text. Click on the title below to find the original essay by Ramesh Srinivasan.
Local-Global: Reconciling Mismatched Ontologies in Development Information Systems
Abstract
This paper extends pre-existing digital divide conceptualizations to further investigate the important issue of mismatches between the ontologies of state-created information systems and local, community preferences. We argue that the reconciliation of these diverse logics and framings is critical for the effective engagement with communities as well as formulation and implementation of development policies around information systems. We suggest several paths toward overcoming mismatched ontologies that would enable communities to be directly involved and productively engaged in developing shared ontologies. These mechanisms would also help policymakers to avoid ‘information loss’ of ontology mismatches while preserving their ability to develop scalable, comparative perspectives to guide policies.
1. Introduction
“Waterlogging” is a perennial complaint in cities in Karnataka, India. A few hours of rain can turn a dry street to a rushing torrent, while a burst pipe or a blocked drain can turn a pedestrian crossing into a treacherous lake. Local newspapers are full of photos, bus stops and public places full of discussion. Yet city data on public grievances contains no record of “waterlogging” – instead there are recorded incidents of storm drains in need of desilting, storm drains in need of repair, leaking pipes, choked underground drains. These are the categories that citizens can choose from to report the puddles – which may very well look the same regardless of origin – via cities’ Public Grievance and Redressal Systems. [65]
Communities may in fact form and self-define around shared ontologies, constructed and re-constructed fluidly [50] through shared social and cultural activities and the ever-changing lived experiences of their members. Our use of ontology does not imply a reified nor exoticized model of ‘pastness’ or ‘locality’ that ignores flows of interaction that shape communities over time [2], but merely implies a distinction between groups’ mental maps of their surroundings.
The information loss between communities’ and states’ ontologies, on the other hand, is likely to be greater. The state ‘meta ontology’ sheds much of the local context in order to ensure tractable management for policy purposes including, especially, taxation, defense, provision of infrastructure and services, and economic management.[68,1,46]
Mismatched ontologies contribute to: (a) ineffective delivery of information services to communities; (b) insufficient participation and interaction with local communities; and importantly, and (c) ‘information loss’ that affects states’ abilities to effectively deliver goods, services, and development-supporting interventions.
2. The power of ontologies, the problem of information loss
The problem is especially pervasive for economic development policy, in which states’ goals are (at least normatively) defined in terms of individuals’ utility, or sense of wellbeing. Some of the most prominent formulations of “development” measure progress in terms of achievements that only make sense with reference to individuals’ or communities’ ontologies.
Censuses often group individuals as employed or unemployed, there is no reason that they could not also include categories for happily employed and unhappily employed as well. Governments often base the relations in their ontologies on those derived by the scientific method; there is no reason that they could not also incorporate folkloric relations that guide community perceptions.
The limitations on how much information can feasibly be aggregated through group decision making to determine social choices have been formally and extensively explored in social choice theory. [3, 5]
Every explicit effort to document a territory, such as a census, is based on particular claims of how a community is to be measured, how the boundaries of a community are to be determined, what counts as an activity, and how these collected data points are to be connected and compared. [61, 55] These claims may be motivated by politics [43, 31] or determined by administrative and technological feasibility of data collection, storage, and retrieval.
Researchers and citizens are less able to challenge the meta ontology when they cannot model and demonstrate the validity of local restrictions, practices, events, and entities according to community ontologies. States’ dominant position in the supply of data will likely change over time as the costs of collection, compilation, storage, and dissemination of community-produced data continue to decline. But even then, states’ authority may privilege conclusions drawn from “official” versus non-state produced data.
This discussion, therefore, leaves us with some important questions concerning the extent to which data models and sociotechnical systems optimize between local sustainability and cross-community scaleability? Or, is there a way in which community activities can be viewed and monitored from the birds-eye by the states while still preserving the local nuances?
3. Bridging local and global
We close this paper by reflecting on several ways in which information loss can be reduced. This section offers three possibilities, each the basis of ongoing research:
1) Developing collaborative and inclusive ontologies. Systems that engage communities to dynamically model their relationship to the information they are provided, around local categories, and fluid relationships between these, have been used sustainably and innovatively in cross-cultural local community contexts [e.g; 34, 45, 37]
2) Harnessing technology to ensure more effective dissemination of existing information in a form that enables communities to engage with and reorganize data in accordance with local ontologies.
3) Rethinking policymaking to decentralize more decision making to subnational and local governments that may operate on ‘less-meta’ ontologies that lose less information relative to community ontologies.
3.1. Collaborative and inclusive ontologies
Fluid ontologies, in their most localized form, involve content creators and multiple stakeholders in the direct crafting of categories and data representations so as to ensure that the information they interact so as to ensure that information is presented, retrieved, preserved, and shared around relevant categorical and relational attributes that are sensible to the community in particular [50].
We believe that these creative and local uses of tagging, rating, and other types of Web 2.0 technologies present powerful opportunities to adapt and edit a meta ontology and reconcile it with local practices.
3.2. Improved and interactive dissemination
Technology is also important for ensuring that information dissemination is as flexible as possible, so that communities can interact with the data stored in meta-ontologies in the manner that they see fit.
3.3. Institutional design
Policy decentralization can mitigate information loss by empowering decisionmakers with ‘less meta’ ontologies to respond to community needs.
….dark side, however, in that it can allow locally powerful groups to unduly influence policies in their favor rather than the community interest. [Grindle])
3. Conclusions
We have argued that information loss due to mismatch between community ontologies and the meta ontologies that states act upon has serious consequences for the efficacy of state policies, especially those aimed at accelerating development.
We are not the first to point out the defects of centralized planning and the hubris of states.
The paper does offer a new perspective on this long-recognized problem, however, by re- conceptualizing information loss as a kind of communication failure that can be increasingly mitigated through technology as well as addressed through institutional redesign.
Normative considerations, aside, progress in reducing information loss ultimately comes down to understanding the positive political economy of states’ efforts to form, maintain, and rely on data organized in meta ontologies as the basis for action. What are states’ incentives to adopt recommendations such as those mentioned above, and can they find a point of reconciliation with community-driven, local ontologies? This remains the key question.
