Braess' paradox or Braess's paradox is a proposed explanation for a seeming improvement to a road network being able to impede traffic through it. It was discovered in 1968 by mathematician Dietrich Braess, who noticed that adding a road to a congested road traffic network could increase overall journey time, and it has been used to explain instances of improved traffic flow when existing major roads are closed. The paradox may have analogues in electrical power grids and biological systems. It has been suggested that in theory, the improvement of a malfunctioning network could be accomplished by removing certain parts of it.

## Discovery and definition

Dietrich Braess, a mathematician at Ruhr University, Germany, noticed the flow in a road network could be impeded by adding a new road, when he was working on traffic modelling. His idea was that if each driver is making the optimal self-interested decision as to which route is quickest, a shortcut could be chosen too often for drivers to have the shortest travel times possible. More formally, the idea behind Braess' discovery is that the Nash equilibrium may not equate with the best overall flow through a network.[1]

The paradox is stated as follows:

"For each point of a road network, let there be given the number of cars starting from it, and the destination of the cars. Under these conditions one wishes to estimate the distribution of traffic flow. Whether one street is preferable to another depends not only on the quality of the road, but also on the density of the flow. If every driver takes the path that looks most favorable to him, the resultant running times need not be minimal. Furthermore, it is indicated by an example that an extension of the road network may cause a redistribution of the traffic that results in longer individual running times."

Adding extra capacity to a network when the moving entities selfishly choose their route can in some cases reduce overall performance. That is because the Nash equilibrium of such a system is not necessarily optimal. The network change induces a new game structure which leads to a (multiplayer) prisoner's dilemma. In a Nash equilibrium, drivers have no incentive to change their routes. While the system is not in a Nash equilibrium, individual drivers are able to improve their respective travel times by changing the routes they take. In the case of Braess' paradox, drivers will continue to switch until they reach Nash equilibrium despite the reduction in overall performance.

If the latency functions are linear, adding an edge can never make total travel time at equilibrium worse by a factor of more than 4/3.[2]

## Possible instances of the paradox in action

### Prevalence

In 1983, Steinberg and Zangwill provided, under reasonable assumptions, the necessary and sufficient conditions for Braess' paradox to occur in a general transportation network when a new route is added. (Note that their result applies to the addition of any new route, not just to the case of adding a single link.) As a corollary, they obtain that Braess' paradox is about as likely to occur as not occur; their result applies to random rather than planned networks and additions.[3]

### Traffic

In Seoul, South Korea, a speeding up in traffic around the city was seen when a motorway was removed as part of the Cheonggyecheon restoration project.[4] In Stuttgart, Germany, after investments into the road network in 1969, the traffic situation did not improve until a section of newly built road was closed for traffic again.[5] In 1990 the temporary closing of 42nd Street in New York City for Earth Day reduced the amount of congestion in the area.[6] In 2008 Youn, Gastner and Jeong demonstrated specific routes in Boston, New York City and London where that might actually occur and pointed out roads that could be closed to reduce predicted travel times.[7] In 2009, New York experimented with closures of Broadway at Times Square and Herald Square, which resulted in improved traffic flow and permanent pedestrian plazas.[8]

### Electricity

In 2012, scientists at the Max Planck Institute for Dynamics and Self-Organization demonstrated, through computational modeling, the potential for the phenomenon to occur in power transmission networks where power generation is decentralized.[9] In 2012, an international team of researchers from Institut Néel (CNRS, France), INP (France), IEMN (CNRS, France) and UCL (Belgium) published in Physical Review Letters[10] a paper showing that Braess' paradox may occur in mesoscopic electron systems. In particular, they showed that adding a path for electrons in a nanoscopic network paradoxically reduced its conductance. That was shown both by theoretical simulations and experiments at low temperature using as scanning gate microscopy.

### Biology

According to Adilson E. Motter possible Braess' paradox outcomes may exist in many biological systems. Motter suggests cutting out part of a damaged network could rescue it. For resource management of endangered species food webs, in which extinction of many species might follow sequentially, a deliberate elimination of a doomed species from the network could be used to bring about the positive outcome of preventing a series of further extinctions.[1]

### Team sports strategy

It has been suggested that in basketball, a team can be seen as a network of possibilities for a route to scoring a basket, with a different efficiency for each pathway, and a star player could reduce the overall efficiency of the team, analogous to a shortcut that is overused increasing the overall times for a journey through a road network. A proposed solution for maximum efficiency in scoring is for a star player to shoot about the same number of shots as teammates.[11]

In soccer Helenio Herrera is well-known for his famous quote "with 10 [players] our team plays better than with 11".

## Example

Consider a road network as shown in the adjacent diagram on which 4000 drivers wish to travel from point Start to End. The travel time in minutes on the Start-A road is the number of travelers (T) divided by 100, and on Start-B is a constant 45 minutes (likewise with the roads across from them). If the dashed road does not exist (so the traffic network has 4 roads in total), the time needed to drive Start-A-End route with A drivers would be ${\displaystyle {\tfrac {A}{100}}+45}$. The time needed to drive the Start-B-End route with B drivers would be ${\displaystyle {\tfrac {B}{100}}+45}$. If either route were shorter, it would not be a Nash equilibrium: a rational driver would switch routes from the longer route to the shorter route. As there are 4000 drivers, the fact that ${\displaystyle A+B=4000}$ can be used to derive the fact that ${\displaystyle A=B=2000}$ when the system is at equilibrium. Therefore, each route takes ${\displaystyle {\tfrac {2000}{100}}+45=65}$ minutes.

Now suppose the dashed line is a road with an extremely short travel time of approximately 0 minutes. In that situation, all drivers will choose the Start-A route rather than the Start-B route because Start-A will take only ${\displaystyle {\tfrac {T}{100}}={\tfrac {4000}{100}}=40}$ minutes at its worst, and Start-B is guaranteed to take 45 minutes. Once at point A, every rational driver will elect to take the "free" road to B and from there continue to End because once again A-End is guaranteed to take 45 minutes while A-B-End will take at most ${\displaystyle 0+{\tfrac {4000}{100}}=40}$ minutes. Each driver's travel time is ${\displaystyle {\tfrac {4000}{100}}+{\tfrac {4000}{100}}=80}$ minutes, an increase from the 65 minutes required when the fast A-B road did not exist. No driver has an incentive to switch, as the two original routes (Start-A-End and Start-B-End) are both now 85 minutes. If every driver were to agree not to use the A-B path, every driver would benefit by reducing their travel time by 15 minutes. However, because any single driver will always benefit by taking the A-B path, the socially optimal distribution is not stable and so Braess' paradox occurs.

## Existence of an equilibrium

If one assumes the travel time for each person driving on an edge to be equal, an equilibrium will always exist.

Let ${\displaystyle L_{e}(x)}$ be the formula for the travel time of each person traveling along edge ${\displaystyle e}$ when ${\displaystyle x}$ people take that edge. Suppose there is a traffic graph with ${\displaystyle x_{e}}$ people driving along edge ${\displaystyle e}$. Let the energy of e, ${\displaystyle E(e)}$, be

${\displaystyle \sum _{i=1}^{x_{e}}L_{e}(i)=L_{e}(1)+L_{e}(2)+\cdots +L_{e}(x_{e})}$

(If ${\displaystyle x_{e}=0}$ let ${\displaystyle E(e)=0}$). Let the total energy of the traffic graph be the sum of the energies of every edge in the graph.

Take a choice of routes that minimizes the total energy. Such a choice much exist because there are finitely many choices of routes. That will be an equilibrium.

Assume, for contradiction, this is not the case. Then, there is at least one driver who can switch the route and improve the travel time. Suppose the original route is ${\displaystyle e_{0},e_{1},\ldots ,e_{n}}$ while the new route is ${\displaystyle e'_{0},e'_{1},\ldots ,e'_{m}}$. Let ${\displaystyle E}$ be total energy of the traffic graph, and consider what happens when the route ${\displaystyle e_{0},e_{1},...e_{n}}$ is removed. The energy of each edge ${\displaystyle e_{i}}$ will be reduced by ${\displaystyle L_{e_{i}}(x_{e_{i}})}$ and so the ${\displaystyle E}$ will be reduced by ${\displaystyle \sum _{i=0}^{n}L_{e_{i}}(x_{e_{i}})}$. That is simply the total travel time needed to take the original route. If the new route is then added, ${\displaystyle e'_{0},e'_{1},\ldots ,e'_{m}}$, the total energy ${\displaystyle E}$ will be increased by the total travel time needed to take the new route. Because the new route is shorter than the original route, ${\displaystyle E}$ must decrease relative to the original configuration, contradicting the assumption that the original set of routes minimized the total energy.

Therefore, the choice of routes minimizing total energy is an equilibrium.

## Finding an equilibrium

The above proof outlines a procedure known as best response dynamics, which finds an equilibrium for a linear traffic graph and terminates in a finite number of steps. The algorithm is termed "best response" because at each step of the algorithm, if the graph is not at equilibrium then some driver has a best response to the strategies of all other drivers and switches to that response.

Pseudocode for Best Response Dynamics:

 Let P be some traffic pattern.
while P is not at equilibrium:
compute the potential energy e of P
for each driver d in P:
for each alternate path p available to d:
compute the potential energy n of the pattern when d takes path p
if n < e:
modify P so that d takes path p
continue the topmost while


At each step, if some particular driver could do better by taking an alternate path (a "best response"), doing so strictly decreases the energy of the graph. If no driver has a best response, the graph is at equilibrium. Since the energy of the graph strictly decreases with each step, the best response dynamics algorithm must eventually halt.

## How far from optimal is traffic at equilibrium?

If the travel time functions are linear, that is ${\displaystyle L_{e}(x)=a_{e}x+b_{e}}$ for some ${\displaystyle a_{e},b_{e}\geq 0}$, then at worst, traffic in the energy-minimizing equilibrium is twice as bad as socially optimal.[12]

Proof: Let Z be some traffic configuration, with associated energy E(Z) and total travel time T(Z). For each edge, the energy is the sum of an arithmetic progression, and using the formula for the sum of an arithmetic progression, one can show that E(Z) ≤ T(Z) ≤ 2E(Z). If ${\displaystyle Z_{o}}$ is the socially-optimal traffic flow and ${\displaystyle Z_{e}}$ is the energy-minimizing traffic flow, the inequality implies that ${\displaystyle T(Z_{e})\leq 2E(Z_{e})\leq 2E(Z_{o})\leq 2T(Z_{o})}$.

Thus, the total travel time for the energy-minimizing equilibrium is at most twice as bad as for the optimal flow.

## Dynamics analysis of Braess' paradox

In 2013, Dal Forno and Merlone [13] interpret Braess' paradox as a dynamical ternary choice problem. The analysis shows how the new path changes the problem. Before the new path is available, the dynamics is the same as in binary choices with externalities, but the new path transforms it into a ternary choice problem. The addition of an extra resource enriches the complexity of the dynamics. In fact, there can even be coexistence of cycles, and the implication of the paradox on the dynamics can be seen from both a geometrical and an analytical perspective.

## References

1. ^ a b New Scientist, 42nd St Paradox: Cull the best to make things better, 16 January 2014 by Justin Mullins
2. ^ Roughgarden, Tim; Tardos, Éva. "How Bad is Selfish Routing?" (PDF). Journal of the ACM. Archived (PDF) from the original on 2016-04-09. Retrieved 2016-07-18.
3. ^ Steinberg, R.; Zangwill, W. I. (1983). "The Prevalence of Braess' Paradox". Transportation Science 17 (3): 301. doi:10.1287/trsc.17.3.301.
4. ^ Easley, D.; Kleinberg, J. (2008). Networks. Cornell Store Press. p. 71.
5. ^ Knödel, W. (31 January 1969). Graphentheoretische Methoden Und Ihre Anwendungen. Springer-Verlag. pp. 57–59. ISBN 978-3-540-04668-4.
6. ^ Kolata, Gina (1990-12-25). "What if They Closed 42d Street and Nobody Noticed?". New York Times. Retrieved 2008-11-16.
7. ^ Youn, Hyejin; Gastner, Michael; Jeong, Hawoong (2008). "Price of Anarchy in Transportation Networks: Efficiency and Optimality Control". Physical Review Letters 101 (12): 128701. arXiv:0712.1598. Bibcode:2008PhRvL.101l8701Y. doi:10.1103/PhysRevLett.101.128701. ISSN 0031-9007. PMID 18851419. External link in |title= (help)
8. ^ http://www.uh.edu/engines/epi2814.htm
9. ^ Staff (Max Planck Institute) (September 14, 2012), "Study: Solar and wind energy may stabilize the power grid", R&D Magazine, rdmag.com, retrieved September 14, 2012
10. ^ Pala, M. G.; Baltazar, S.; Liu, P.; Sellier, H.; Hackens, B.; Martins, F.; Bayot, V.; Wallart, X.; Desplanque, L.; Huant, S. (2012) [6 Dec 2011 (v1)]. "Transport Inefficiency in Branched-Out Mesoscopic Networks: An Analog of the Braess Paradox". Physical Review Letters 108 (7). arXiv:1112.1170. Bibcode:2012PhRvL.108g6802P. doi:10.1103/PhysRevLett.108.076802. ISSN 0031-9007.
11. ^ The price of Anarchy in Basketball, Brian Skinner
12. ^ Easley, David; Kleinberg, Jon. "Networks, Crowds, and Markets: Reasoning about a Highly Connected World (8.3 Advanced Material: The Social Cost of Traffic at Equilibrium)" (PDF). Jon Kleinberg's Homepage. Jon Kleinberg. Archived (PDF) from the original on 2015-03-16. Retrieved 2015-05-30. - This is the preprint of ISBN 9780521195331
13. ^ Dal Forno, Arianna; Merlone, Ugo (2013). "Border-collision bifurcations in a model of Braess paradox". Mathematics and Computers in Simulation 87: 1–18. doi:10.1016/j.matcom.2012.12.001. ISSN 0378-4754.