International Conference on Algorithmic Aspects in Information and Management (AAIM) is intended to provide a forum for researchers working in design and analysis of algorithms, game theory, and combinatorial optimization to present their newest results.
In AAIM 2009 conference which is being held in San Francisco, on of my co-authors will be presenting our paper in the game theory session. The title of the paper is “Power indices of spanning connectivity games” and is joint work with O. Lachish, M. Paterson and R. Savani.

In the paper, we study the problem of computing the influence of edges in a network. Game theorists have studied notions of efficiency, fairness and stability extensively. Therefore, it is only natural that when applications in computer science and multiagent systems require fair and stable allocations, social choice theory and cooperative game theory provide appropriate foundations. For example, a network administrator with limited resources to maintain the links in the network may decide to commit resources to links according to their connecting ability. A surveillance network comprises communication channels. In order to intercept messages on the channels, resources may be utilized according to the ability of a channel to connect all groups. In a social network, we may be interested in checking which connections are more important in maintaining connectivity and hence contribute more to social welfare. The paper is in the line of recent work where economic paradigms are used in computer science applications where there are multiple agents with with their own strategic motives.

ACM’s most prestigious technical award is accompanied by a prize of $250,000. It is given to an individual selected for contributions of a technical nature made to the computing community. The contributions should be of lasting and major technical importance to the computer field. Financial support of the Turing Award is provided by the Intel Corporation and Google Inc.

The 2008 Turing Award has been given to Barbara Liskov for contributions to practical and theoretical foundations of programming language and system design, especially related to data abstraction, fault tolerance, and distributed computing.

The BBC has also covered the award and has summarized Liskov’s achievements:

Professor Liskov’s design innovations have, over the decades, made software more reliable and easier to maintain. She has invented two computer progamming languages: CLU, a forerunner of modern object-oriented ones and Argus, a distributed programming language. Liskov’s groundbreaking research underpins virtually every modern computer application, forming the basis of modern programming languages such as Java, C# and C++. One of the biggest impacts of her work came from her contributions to the use of data abstraction, a method for organising complex programs.

No math formula will ever model whole biological organisms, economies, ecologies, or large, live networks. Will the Algorithm come to the rescue? This is the next great hope. The algorithmic lens on science is full of promise—and pitfalls.

The main concern in algorithms research is how to do something correctly and optimally. An ‘algorithmic mindset’ is not only essential in problem solving but also helpful in daily life in organization, logical thought process and time planning. The importance of algorithms is well understood within computer science and operations research. What is good news is that this is now being noticed in the media. The Guardian features an article on the pervasiveness of algorithms and ‘how mathematical recipes dictate how we lead our lives’:

In fact, large bits of modern lives are secretly underpinned by complex algorithms – the mathematical equivalent of cookery recipes, which take a piece of information and turn it into an action or decision. Algorithms are like computer programs or flowcharts – a sequence of steps that examines what is happening and comes to a conclusion. Take traffic lights: the computer controlling them asks a series of related questions. What time of day is it? When did the lights last go red? Has a pedestrian pushed the button at the crossing? The algorithm guides the computer, step by step, to change the lights.

The article goes on to show how algorithms come into play in air-traffic control, online purchasing, supermarket operations and almost every part of our daily life.You may read the whole article here. Interestingly, the same theme was covered in the Economist.

The short answer is that graphs can be used to reason symbolically about any kind of pairwise relationship between any kind of entity, and that we like to think about pairwise relationships because unary relationships aren’t powerful enough and k-way relationships for k>2 add extra complication without adding any real power.

We have graphs representing people and social networks connecting them (online friendships, sexual contacts, parenthood, coauthorship, etc). We have graphs representing subroutines in a computer program and caller-callee relations between them. We have graphs representing web pages and html links between them. We have graphs representing proteins in your body and the chemical interactions they participate in. Etc etc.

OxDE also gives a nice explanation of why abstracting problems is useful in mathematics and computer science:

Graphs are powerful because the same kinds of problems and algorithms turn out to be important in many of these different applications. So by taking away the application-specific features of all of those different problems and turning them into something as abstract as a graph, we only have to solve these problems once instead of repeatedly solving the same problems in different disguises.

1. Good mathematical problem-solving (e.g. a major breakthrough on an important mathematical problem);
2. Good mathematical technique (e.g. a masterful use of existing methods, or the development of new tools)
3. Good mathematical theory (e.g. a conceptual framework or choice of notation which systematically unifies and generalises an existing body of results);
4. Good mathematical insight (e.g. a major conceptual simplification, or the realisation of a unifying principle, heuristic, analogy, or theme);
5. Good mathematical discovery (e.g. the revelation of an unexpected and intriguing new mathematical phenomenon, connection, or counterexample);
6. Good mathematical application (e.g. to important problems in physics, engineering, computer science, statistics, etc., or from one field of mathematics to another);
7. Good mathematical exposition (e.g. a detailed and informative survey on a timely mathematical topic, or a clear and well-motivated argument);
8. Good mathematical pedagogy (e.g. a lecture or writing style which enables others to learn and do mathematics more effectively, or contributions to mathematical education);
9. Good mathematical vision (e.g. a long-range and fruitful program or set of conjectures);
10. Good mathematical taste (e.g. a research goal which is inherently interesting and impacts important topics, themes, or questions);
11. Good mathematical public relations (e.g. an effective showcasing of a mathematical achievement to non-mathematicians, or from one field of mathematics to another);
12. Good meta-mathematics (e.g. advances in the foundations, philosophy, history, scholarship, or practice of mathematics);
13. Rigorous mathematics (with all details correctly and carefully given in full);
14. Beautiful mathematics (e.g. the amazing identities of Ramanujan; results which are easy (and pretty) to state but not to prove);
15. Elegant mathematics (e.g. Paul Erdos’ concept of “proofs from the Book”; achieving a difficult result with a minimum of effort);
16. Creative mathematics (e.g. a radically new and original technique, viewpoint, or species of result);
17. Useful mathematics (e.g. a lemma or method which will be used repeatedly in future work on the subject);
18. Strong mathematics (e.g. a sharp result that matches the known counterexamples, or a result which deduces an unexpectedly strong conclusion from a seemingly weak hypothesis);
19. Deep mathematics (e.g. a result which is manifestly non-trivial, for instance by capturing a subtle phenomenon beyond the reach of more elementary tools);
20. Intuitive mathematics (e.g. an argument which is natural and easily visualisable);
21. Definitive mathematics (e.g. a classification of all objects of a certain type; the final word on a mathematical topic)

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as a merger of three highly respected individual conferences: the International Conference in Autonomous Agents, the International Workshop on Agent Theories, Architectures, and Languages, and the International Conference on Multi-Agent Systems. The aim of the joint conference is to provide a single, high-profile, internationally respected archival forum for research in all aspects of the theory and practice of autonomous agents and multi-agent systems.

Although the conference is AI oriented, there is a good portion of papers from a theoretical computer science, algorithms and game theory perspective. It will also be a good opportunity to listen to some really interesting presentations and tutorials. There are also accompanying workshops.

The conference also has a doctoral training program especially for final year PhDs. However, I did not register for that. This is because the training program required too much extra paper work for participation. Moreover, if I want to get advice I can just go and ask the person concerned. Academics are generally very approachable people.

Budapest itself is an amazing city and I look forward to taking some time out to look around.

These two areas are broadly covered in the Panel Section. Theoretical computer science was covered both in the ‘Computer Science and Information’ report and the ‘Pure Mathematics section’. One particularly positive point was that the research area of algorithms and complexity got a special mention and some positive feedback:

There was a significant increase in research in algorithms and complexity, including the establishment of several new centres of excellence. This increased activity was not, however, limited to the research groups specialising in this area, but permeated research in many other areas, such as data-mining, vision, evolutionary computing, agents, etc

…

The research examined was generally much more rigorous that in 2001. There was a more widespread use of mathematics, e.g., to establish the complexity, soundness, completeness and termination of algorithms.

Since, there is currently a major debate on impact factors and citations, the RAE panel noted that citations cannot be used to compare different fields:

We frequently found that citation counts were poorly correlated with the sub-panel’s assessment of the impact of the work examined. Citations also varied widely between research areas. For instance, much of the highly significant theoretical research, in which the UK is world leading, typically attracts low citation counts. Despite these low citations, the work is often found to have profound long-term impact on practical aspects of the field.

…Argonne colleagues have created a new set of simulations called “agent-based models” to better anticipate how markets behave. These new models rely on information gleaned in part from surveys that ask respondents about the factors that influence the way they make decisions. By gaining a more precise understanding of the behavior patterns of individual actors in a market, for example, how willing they are to accept risk, how strongly they value the future or how much time and effort they are able to spend making decisions, researchers and economists can better predict and avoid meltdowns.

Agent-based models separately calculate likely decisions for each individual actor in a model, then take the results of these decisions and see what impact they have on other agents. By doing so, they have the potential to foresee a panic, a protracted “hot streak,” herd mentality or a number of other market phenomena that pure rational-actor models would tend to miss.

She observes that the internet has driven the research in computer science and economics. She also observes that the Internet has the characteristics of an economy as well as those of a massive computer. It is no surprise that algorithmic game theory is a rapidly growing field. Feigenbaum gives the example of online auctions where both economists and computer scientists have much to contribute. She sums up the benefit of an algorithmic and complexity approach to problems in economic and commerce in a nice way:

The main focus of computer science theory has been on how to compute things efficiently. If you’re doing something on a single computer, how fast can you do it? How little memory can you use? If you’re doing something on a network, how much bandwidth do you need? How much coordination do you have to achieve between the different nodes? Conversely, efficient computation has not been the focus of economic theory. What’s happening now is that computer scientists are paying attention to incentives, and economists are paying attention to computational and communication efficiency.

Computer Scientists’ interest in economic theory and application can also be seen by special interest groups such as ACM Sigecom.

The British Colloquium for Theoretical Computer Science (BCTCS) is an annual event for UK-based researchers in theoretical computer science. A central aspect of BCTCS is the training of PhD students, providing an environment for students to gain experience in presenting their work, to broaden their outlook on the subject, and to benefit from contact with established researchers.

BCTCS 2009 is being organised by Artur Czumaj, Sara Kalvala, and Steve Matthews. Having attended two previous BCTCS, I realized that BCTCS is one of the annual meetings to look forward to for theoretical computer scientists in the UK. The speaker list is always excellent with a good representation of both formal methods and algorithmics. The following are some of the invited speakers for the upcoming event:

• Noga Alon, Tel Aviv University
• Paul Goldberg, University of Liverpool
• Andy Gordon, Microsoft Research
• Jane Hillston, University of Edinburgh
• Bill Wadge, University of Victoria

BCTCS is a particularly good chance for research students to get exposure. Every one gets a chance to talk about their current research in a relatively informal setting. BCTCS 2009 has secured funding for up to 45 students. Therefore if you are a PhD student working in theoretical computer science, keep a look out for the call for early registration.