HaPoC Symposium at IACAP-14

It is our pleasure to announce that we will organize a HaPoC symposium during the
IACAP-14 conference.

Since the first HaPoC conference in 2011, the community of people interested in HaPoC is thriving and a large number of different events has been organized. The general spirit of these events is interdisciplinarity and openness towards different fields relevant to HaPoC, guided by a quote by Mike Mahoney that the computer is not one thing but many things and that the same holds true of computing. We were and are strongly convinced that such trans- and interdisciplinarity is necessary if one wants to reflect on a discipline such as computer science with its multidimensional nature.

The current symposium is organized in a similar manner. The programme consists of researchers coming from a diversity of backgrounds, who want to engage with topics relevant to the history and philosophy of computing. The meeting will focus on the following questions:

– What are programs, algorithms, machines and how do we understand their languages?
– What is computing/computation?
– What is the science in computer science?

Clearly, these questions can be tackled from a diversity of perspectives. For this reason, one historian, one philosopher and one mathematician/computer scientist are invited to deliver a talk for each of these three fundamental questions.


Barry Cooper (University of Leeds)
Computing the Rainbow

To what extent is philosophy computation? Is computation necessarily precise and semantically destructive? In this talk we tour various aspects of computation: Embodiment; representation; description and definition; and the roles and means of observation and control. We ask: Does computation exist without embodiment? Or without representation? What in broad terms is the relationship between description, logic and computation? And to what extent does our everyday use of natural language qualify as computation, or is it something else? How does the mathematics of information relate to this question? Can one, and should one, disentangle the computational
roles of logic and information? And is the concept of causality clarified in such a computational setting? We also look at how our attempts at answers play out in relation to the history and theory of familiar computational hosts: Biology; the brain and mentality; economics; the internet and embodiments of the classical Turing model of computation; and in regard to the physical universe itself.

Nachum Dershowitz (Tel Aviv University)
What is concurrent computing?

I will describe a model of computation, based on abstract state machines, that incorporates cooperation between components and encompasses a broad variety of contemporary parallel and distributed models.

Gonzalo Genova (Universidad Carlos III de Madrid, España & Universidad de Santiago de Chile, Chile)
Intertwining of formal and empirical methods in software engineering

What is the science in computer science? Computer Science is a very broad term that encompasses a plurality of research areas and methods, both in “pure” science and in applied science (i.e. engineering). In particular, empirical methods are not enough to account for all kinds of scientific activity in computing. In this talk I will present a three dimensional categorization of research methods in computing: formal-empirical, science-engineering, machine-human. My final focus is on the due consideration of human factors in software engineering research.

Robin Hill (University of Wyoming)
What an Algorithm Is: The Ante-Digital View

An algorithm is a human construct, a finite, abstract, mechanical and imperative control structure. Although its correspondence with the Turing Machine, also a finite abstract mechanical control structure, is widely accepted among theorists, a focus on the intuitive view reveals a full-blown conceptual object that has its own characteristics, such as the imperative form, that have no direct analogy in formal objects. Programs are the articulations of algorithms in a particular medium. The other traditional areas of philosophy―ethics, epistemology, and other questions of metaphysics―can also be applied to computer science in ways outside of the formal that remain largely unexplored, promising a greater understanding and appreciation of computation and its place.

Simone Martini (University of Bologna)
Ipsa forma est substantia. Language(s) as a foundation for computer science

Computer science shares with other disciplines concepts and methods for problem solving. Its distinctive contribution to these common methodologies is the language for doing them. What we (often dismissively)
call programming languages are powerful tools for the modeling of reality which scale at several abstraction levels. I will argue that this is one of the more pervasive contributions of computer science, and that
we can talk of programs, algorithms, and machines only at this linguistic level.

Wilfried Sieg (Carnegie Mellon)
What is the concept of computation?

The classical approach to the effective calculability of number theoretic functions led, through Gödel and Church, to a notion of computability in formal calculi and then to metamathematical absoluteness theorems. The classical approach to the mechanical decidability of problems concerning syntactic configurations led, through Turing and Post, to a notion of computability in formal calculi and then to metamathematical representation theorems. The crucial differences between the calculi used for effective calculability, respectively mechanical decidability are the background to formulating an abstract concept of a computable dynamical system. This concept articulates finiteness and locality conditions that are satisfied by the standard concrete notion of computation. In addition, a representation theorem can be established: the computations of any concrete system falling under the abstract concept can be simulated by a Turing machine.

Ray Turner (University of Essex)
The design and construction of computational artefacts

Computer scientists construct things. They construct software, computers, tablets, embedded systems, chips, type inference frameworks, natural language systems, compilers and interpreters etc. These are the technical artefacts of computer science, computational artefacts. A central activity of the subject concerns their specification, design and construction. On the face of it this is a design activity. But saying this leaves many questions unanswered. What is a good design? What are the methodologies employed in getting from function to structure? How do we evaluate them? Are some computational artefacts abstract and some physical? What is the role of mathematics in the process? What is correctness for computational artefacts? Is the latter mathematical or empirical in nature? What roles do model/theory construction and experimentation play in the activity? Do these roles justify the word science in its title?

Mate Szabo (Carnegie Mellon)
Turing’s Machines and Post’s Canonical Forms.

In 1936 Alan Turing and Emil Post independently introduced strikingly similar models of computation. Although usually Turing’s machines are used as the mathematical model of computation, in most cases they are represented as Post’s Canonical Forms. Indeed, Turing in his (1950) adopted Post’s formulation of Turing machines from his (1947) and described the notion of “logical computing machines” as a notion which was introduced by “Post (1936) and the author (1936).” The prevalent view interprets the events of 1936 as a surprising coincidence, but Davis & Sieg explain it in their (2014) as a result of a deep conceptual confluence. In my talk I will analyze this conceptual confluence on the one hand, and the different approaches Turing and Post took to the intuitive notion of computability or finite combinatory processes on the other.

Davis, Martin and Wilfried Sieg. 2014. “Conceptual Confluence in 1936: Post & Turing.” (Forthcoming) In Thomas Strahm and Giovanni Sommaruga (eds) Turing Centenary Volume. Basel: Birkhäuser.

Post, Emil. 1936. “Finite Combinatory Processes – Formulation 1.” The Journal of Symbolic Logic 1, no. 3: 103-105.

Post, Emil. 1947. “Recursive Unsolvability of a Problem of Thue.” The Journal of Symbolic Logic 12, no. 1: 1-11.

Turing, Alan. 1936. “On Computable Numbers, With an Application to the Entscheidungsproblem.” Proceedings of the London Mathematical Society, Series 2, Vol. 42: 230-265.

Turing, Alan. 1950. “The Word Problem in Semi-Groups With Cancellation.” The Annals of Mathematics, Series 2, Vol. 52, no. 2: 491-505.

Ksenia Tatarchenko (Columbia University)
Computing and the Sands of Time: from al-Khwarazm to Los Alamos.

The history of computer science was born in two famous deserts: the cradle of the atomic bomb became the first official site for memory construction in computing; and Urgench – the administrative center of the Khoresmskaia oblast’ in Uzbek SSR – a remote region known as a birth place of the medieval astronomer and mathematician Al Khwarizmi was chosen as the “eternal return” destination for all information technology specialists. While the Soviet-American detente provided a unique context that allowed for international co-creation of a common heroic origin for a new discipline, computer science, its abrupt end in 1980 with the Soviet invasion of Afghanistan contributed to the establishment of the separated national narratives that persist in the contemporary English and Russian language historiographies. In this paper I analyze two international gatherings, the 1976 conference on the history of computing hosted by N. Metropolis in Los Alamos, and the 1978 pilgrimage to Urgench organized by D. Knuth and A. Ershov, and trace multiple echoes of these events in discipline consolidation, Cold War scientific networks, and politics of remembering.

This event is kindly sponsored by the
Division of History of Science and Technology of the International Union of History and Philosophy of Science
and by the
science-and-technology/index.aspSchool of Science and Technology at Middlesex University London (UK)