Design as Symbolic Violence. Design for Social Justice.

Design embeds ideas in communication and artefacts in subtle and psychologically powerful ways. Sociologist Pierre Bourdieu coined the term ‘symbolic violence’ to describe how powerful ideologies, priorities, values and even sensibilities are constructed and reproduced through cultural institutions, processes and practices. Through symbolic violence, individuals learn to consider unjust conditions as natural and even come to value customs and ideas that are oppressive. Symbolic violence normalises structural violence and enables real violence to take place, often preceding it and later justifying it. Feminist, class, race and indigenous scholars and activists describe how oppressions (how patriarchy, racism, colonialism, etc.) exist within institutions and structures, and also within cultural practices that embed ideologies into everyday life. The theory of symbolic violence sheds light on how design can function to naturalise oppressions and then obfuscate power relations around this process. Through symbolic violence, design can function as an enabler for the exploitation of certain groups of people and the environment they (and ultimately ‘we’) depend on to live. Design functions as symbolic violence when it is involved with the creation and reproduction of ideas, practices, tools and processes that result in structural and other types of violence (including ecocide). Breaking symbolic violence involves discovering how it works and building capacities to challenge and transform dysfunctional ideologies, structures and institutions. This conversation will give participants an opportunity to discuss, critique and/or develop the theory of design as symbolic violence as a basis for the development of design strategies for social justice.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.