Schematic Grothendieck Representation

A spectral Grothendieck representation Rep is said to be schematic if for every triple γ ≤ τ ≤ δ in Top(A), for every A in R^{^}(Ring) we have a commutative diagram in R^{^}:

 

If Rep is schematic, then, P : Top(A) → R^{^} is a presheaf with values in R^{^} over the lattice Top(A)^o, for every A in R.

The modality is to restrict attention to Tors(Rep(A)); that is, a lattice in the usual sense; and hence this should be viewed as the commutative shadow of a suitable noncommutative theory.

For obtaining the complete lattice Q(A), a duality is expressed by an order-reversing bijection: (−)⁻¹ : Q(A) → Q((Rep(A))^o). (Rep(A))^o is not a Grothendieck category. It is additive and has a projective generator; moreover, it is known to be a varietal category (also called triplable) in the sense that it has a projective regular generator P, it is co-complete and has kernel pairs with respect to the functor Hom(P, −), and moreover every equivalence relation in the category is a kernel pair. If a comparison functor is constructed via Hom(P, −) as a functor to the category of sets, it works well for the category of set-valued sheaves over a Grothendieck topology.

Now (−)⁻¹ is defined as an order-reversing bijection between idempotent radicals on Rep(A) and (Rep(A))^o, implying we write (Top(A))⁻¹ for the image of Top(A) in Q((Rep( A))^o). This is encoded in the exact sequence in Rep(A):

0 → ρ(M) → M → ρ⁻¹(M) → 0

(reversed in (Rep(A))^o). By restricting attention to hereditary torsion theories (kernel functors) when defining Tors(−), we introduce an asymmetry that breaks the duality because Top(A)⁻¹ is not in Tors((Rep(A))^op). If notationally, TT(G) is the complete lattice of torsion theories (not necessarily hereditary) of the category G; then (TT(G))⁻¹ ≅ TT(G^op). Hence we may view Tors(G)⁻¹ as a complete sublattice of TT(G^op).

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Black Hole Entropy in terms of Mass. Note Quote.

If M-theory is compactified on a d-torus it becomes a D = 11 – d dimensional theory with Newton constant

G_D = G₁₁/L_d = l⁹₁₁/L_d —– (1)

A Schwartzschild black hole of mass M has a radius

R_s ~ M^(1/(D-3)) G_D^(1/(D-3)) —– (2)

According to Bekenstein and Hawking the entropy of such a black hole is

S = Area/4G_D —– (3)

where Area refers to the D – 2 dimensional hypervolume of the horizon:

Area ~ R_s^D-2 —– (4)

Thus

S ~ 1/G_D (MG_D)^(D-2)/(D-3) ~ M^(D-2)/(D-3) G_D^1/(D-3) —– (5)

From the traditional relativists’ point of view, black holes are extremely mysterious objects. They are described by unique classical solutions of Einstein’s equations. All perturbations quickly die away leaving a featureless “bald” black hole with ”no hair”. On the other hand Bekenstein and Hawking have given persuasive arguments that black holes possess thermodynamic entropy and temperature which point to the existence of a hidden microstructure. In particular, entropy generally represents the counting of hidden microstates which are invisible in a coarse grained description. An ultimate exact treatment of objects in matrix theory requires a passage to the infinite N limit. Unfortunately this limit is extremely difficult. For the study of Schwarzchild black holes, the optimal value of N (the value which is large enough to obtain an adequate description without involving many redundant variables) is of order the entropy, S, of the black hole.

Considering the minimum such value for N, we have

N_min(S) = MR_s = M(MG_D)^1/D-3 = S —– (6)

We see that the value of N_min in every dimension is proportional to the entropy of the black hole. The thermodynamic properties of super Yang Mills theory can be estimated by standard arguments only if S ≤ N. Thus we are caught between conflicting requirements. For N >> S we don’t have tools to compute. For N ~ S the black hole will not fit into the compact geometry. Therefore we are forced to study the black hole using N = N_min = S.

Matrix theory compactified on a d-torus is described by d + 1 super Yang Mills theory with 16 real supercharges. For d = 3 we are dealing with a very well known and special quantum field theory. In the standard 3+1 dimensional terminology it is U(N) Yang Mills theory with 4 supersymmetries and with all fields in the adjoint repersentation. This theory is very special in that, in addition to having electric/magnetic duality, it enjoys another property which makes it especially easy to analyze, namely it is exactly scale invariant.

Let us begin by considering it in the thermodynamic limit. The theory is characterized by a “moduli” space defined by the expectation values of the scalar fields φ. Since the φ also represents the positions of the original DO-branes in the non compact directions, we choose them at the origin. This represents the fact that we are considering a single compact object – the black hole- and not several disconnected pieces.

The equation of state of the system, defined by giving the entropy S as a function of temperature. Since entropy is extensive, it is proportional to the volume ∑³ of the dual torus. Furthermore, the scale invariance insures that S has the form

S = constant T³∑³ —– (7)

The constant in this equation counts the number of degrees of freedom. For vanishing coupling constant, the theory is described by free quanta in the adjoint of U(N). This means that the number of degrees of freedom is ~ N².

From the standard thermodynamic relation,

dE = TdS —– (8)

and the energy of the system is

E ~ N²T⁴∑³ —– (9)

In order to relate entropy and mass of the black hole, let us eliminate temperature from (7) and (9).

S = N²∑³⁽(E/N²∑³))^3/4 —– (10)

Now the energy of the quantum field theory is identified with the light cone energy of the system of DO-branes forming the black hole. That is

E ≈ M²/N R —– (11)

Plugging (11) into (10)

S = N²∑³(M²R/N²∑³)^3/4 —– (12)

This makes sense only when N << S, as when N >> S computing the equation of state is slightly trickier. At N ~ S, this is precisely the correct form for the black hole entropy in terms of the mass.

Catastrophe, Gestalt and Thom’s Natural Philosophy of 3-D Space as Underlying All Abstract Forms – Thought of the Day 157.0

The main result of mathematical catastrophe theory consists in the classification of unfoldings (= evolutions around the center (the germ) of a dynamic system after its destabilization). The classification depends on two sorts of variables:

(a) The set of internal variables (= variables already contained in the germ of the dynamic system). The cardinal of this set is called corank,

(b) the set of external variables (= variables governing the evolution of the system). Its cardinal is called codimension.

The table below shows the elementary catastrophes for Thom:

The A-unfoldings are called cuspoids, the D-unfoldings umbilics. Applications of the E-unfoldings have only been considered in A geometric model of anorexia and its treatment. By loosening the condition for topological equivalence of unfoldings, we can enlarge the list, taking in the family of double cusps (X₉) which has codimension 8. The unfoldings A₃(the cusp) and A₅ (the butterfly) have a positive and a negative variant A₊₃, A_-3, A₊₅, A_-5.

We obtain Thorn’s original list of seven “catastrophes” if we consider only unfoldings up to codimension 4 and without the negative variants of A₃ and A₅.

Thom argues that “gestalts” are locally con­stituted by maximally four disjoint constituents which have a common point of equilibrium, a common origin. This restriction is ultimately founded in Gibb’s law of phases, which states that in three-dimensional space maximally four independent systems can be in equilibrium. In Thom’s natural philosophy, three-dimensional space is underlying all abstract forms. He, therefore, presumes that the restriction to four constituents in a “gestalt” is a kind of cognitive universal. In spite of the plausibility of Thom’s arguments there is a weaker assumption that the number of constituents in a gestalt should be finite and small. All unfoldings with codimension (i.e. number of external variables) smaller than or equal to 5 have simple germs. The unfoldings with corank (i.e. number of internal variables) greater than two have moduli. As a matter of fact the most prominent semantic archetypes will come from those unfoldings considered by René Thom in his sketch of catastrophe theoretic semantics.

Man Proposes OR Woman Proposes – “Matches” Happen Algorithmically and are Economically Walrasian Rather than Keynesian. Note Quote & Didactics.

Consider a set M of men and a set W of women. Each m ∈ M has a strict preference ordering over the elements of W and each w ∈ W has a strict preference ordering over men. Let us denote the preference ordering of an agent i by ≻_i and x ≻_i y will mean that agent i ranks x above y. Now a marriage or matching would be considered as an assignment of men to women such that each man is assigned to at most one woman and vice versa. But, what if the agent decides to remain single. This is possible by two ways, viz. if a man or a woman is matched with oneself, or for each man or woman, there is a dummy woman or man in the set W or M that corresponds to being single. If this were the construction, then, we could safely assume |M| = |W|. But, there is another impediment here, whereby a subversion of sorts is possible, in that a group of agents could simply opt out of the matching exercise. In such a scenario, it becomes mandatory to define a blocking set. As an implication of such subversiveness, a matching is called unstable if there are two men m, m’ and two women w, w’ such that

1. m is matched to w
2. m’ is matched to w’, and
3. w’ ≻_m w and m ≻_w’ m’

then, the pair (m, w’) is a blocking pair. Any matching without the blocking pair is called stable.

Now, given the preferences of men and women, is it always possible to find stable matchings? For the same, what is used is Gale and Shapley’s deferred acceptance algorithm.

So, after a brief detour, let us concentrate on the male-proposal version.

First, each man proposes to his top-ranked choice. Next, each woman who has received at least two proposals keeps (tentatively) her top-ranked proposal and rejects the rest. Then, each man who has been rejected proposes to his top-ranked choice among the women who have not rejected him. Again each woman who has at least two proposals (including ones from previous rounds) keeps her top-ranked proposal and rejects the rest. The process repeats until no man has a woman to propose to or each woman has at most one proposal. At this point the algorithm terminates and each man is assigned to a woman who has not rejected his proposal. No man is assigned to more than one woman. Since each woman is allowed to keep only one proposal at any stage, no woman is assigned to more than one man. Therefore the algorithm terminates in a matching.

Consider the matching {(m₁, w₁), (m₂, w₂), (m₃, w₃)}. This is an unstable matching since (m₁, w₂) is a blocking pair. The matching {(m₁, w₁), (m₃, w₂), (m₂, w₃)}, however, is stable. Now looking at the figure above, m₁ proposes to w₂, m₂ to w₁, and m₃ to w₁. At the end of this round, w₁ is the only woman to have received two proposals. One from m₃ and the other from m₂. Since she ranks m₃ above m₂, she keeps m₃ and rejects m₂. Since m₃ is the only man to have been rejected, he is the only one to propose again in the second round. This time he proposes to w₃. Now each woman has only one proposal and the algorithm terminates with the matching {(m₁, w₂), (m₂, w₃), (m₃, w₂)}.

The male propose algorithm terminates in a stable matching.

Suppose not. Then ∃ a blocking pair (m₁, w₁) with m₁ matched to w₂, say, and w₁ matched to m₂. Since (m₁, w₁) is blocking and w₁ ≻_m1 w₂, in the proposal algorithm, m₁ would have proposed to w₁ before w₂. Since m₁ was not matched with w₁ by the algorithm, it must be because w₁ received a proposal from a man that she ranked higher than m₁. Since the algorithm matches her to m₂ it follows that m₂ ≻_w1 m₁. This contradicts the fact that (m₁, w₁) is a blocking pair.

Even if where the women propose, the outcome would still be stable matching. The only difference is in kind as the stable matching would be different from the one generated when the men propose. This would also imply that even if stable matching is guaranteed to exist, there is more than one such matching. Then what is the point to prefer one to the other? Well, there is a reason:

Denote a matching by μ. The woman assigned to man m in the matching μ is denoted μ(m). Similarly, μ(w) is the man assigned to woman w. A matching μ is male-optimal if there is no stable matching ν such that ν(m) ≻_m μ(m) or ν(m) = μ(m) ∀ m with ν(j) ≻_j μ(j) for at least one j ∈ M. Similarly for the female-optimality.

The stable matching produced by the (male-proposal) Deferred Acceptance Algorithm is male-optimal.

Let μ be the matching returned by the male-propose algorithm. Suppose μ is not male optimal. Then, there is a stable matching ν such that ν(m) ≻_m μ(m) or ν(m) = μ(m) ∀ m with ν(j) ≻_j μ(j) for at least one j ∈ M. Therefore, in the application of the proposal algorithm, there must be an iteration where some man j proposes to ν(j) before μ(j) since ν(j) ≻_j μ(j) and is rejected by woman ν(j). Consider the first such iteration. Since woman ν(j) rejects j she must have received a proposal from a man i she prefers to man j. Since this is the first iteration at which a male is rejected by his partner under ν, it follows that man i ranks woman ν(j) higher than ν(i). Summarizing, i ≻_ν(j) j and ν(j) ≻_i ν(i) implying that ν is not stable, a contradiction.

Now, the obvious question is if this stable matching is optimal w.r.t. to both men and women? The answer this time around is NO. From above, it could easily be seen that there are two stable matchings, one of them is male-optimal and the other is female-optimal. At least, one female is strictly better-off under the female optimality than male optimality, and by this, no female is worse off. If the POV is men, a similar conclusion is drawn.  A stable marriage is immune to a pair of agents opting out of the matching. We could ask that no subset of agents should have an incentive to opt out of the matching. Formally, a matching μ′ dominates a matching μ if there is a set S ⊂ M ∪ W such that for all m, w ∈ S, both (i) μ′(m), μ′(w) ∈ S and (ii) μ′(m) ≻_m μ(m) and μ′(w) ≻_w μ(w). Stability is a special case of this dominance condition when we restrict attention to sets S consisting of a single couple. The set of undominated matchings is called the core of the matching game.

The direct mechanism associated with the male propose algorithm is strategy-proof for the males.

Suppose not. Then there is a profile of preferences π = (≻_m1 , ≻_m2 , . . . , ≻_mn) for the men, such that man m₁, say, can misreport his preferences and obtain a better match. To express this formally, let μ be the stable matching obtained by applying the male proposal algorithm to the profile π. Suppose that m₁ reports the preference ordering ≻_∗ instead. Let ν be the stable matching that results when the male-proposal algorithm is applied to the profile π¹ = (≻_∗, ≻_m2 , . . . , ≻_mn). For a contradiction, suppose ν(m₁) ≻_m1 μ(m₁). For notational convenience let a ≽_m b mean that a ≻_m b or a = b.

First we show that m₁ can achieve the same effect by choosing an ordering ≻̄ where woman ν(m₁) is ranked first. Let π² = (≻̄ , ≻_m2 , . . . , ≻_mn). Knowing that ν is stable w.r.t the profile π¹ we show that it is stable with respect to the profile π². Suppose not. Then under the profile π² there must be a pair (m, w) that blocks ν. Since ν assigns to m₁ its top choice with respect to π², m₁ cannot be part of this blocking pair. Now the preferences of all agents other than m₁ are the same in π¹ and π². Therefore, if (m, w) blocks ν w.r.t the profile π², it must block ν w.r.t the profile π¹, contradicting the fact that ν is a stable matching under π¹.

Let λ be the male propose stable matching for the profile π². ν is a stable matching w.r.t the profile π². As λ is male optimal w.r.t the profile π², it follows that λ(m₁) = ν(m₁).

Let’s assume that ν(m₁) is the top-ranked woman in the ordering ≻_∗. Now we show that the set B = {m_j : μ(m_j) ≻_mj ν(m_j)} is empty. This means that all men, not just m₁, are no worse off under ν compared to μ. Since ν is stable w.r.t the original profile, π this contradicts the male optimality of μ.

Suppose B ≠ ∅. Therefore, when the male proposal algorithm is applied to the profile π¹, each m_j ∈ B is rejected by their match under μ, i.e., μ(m_j). Consider the first iteration of the proposal algorithm where some m_j is rejected by μ(m_j). This means that woman μ(m_j) has a proposal from man m_k that she ranks higher, i.e., m_k ≻_μ(mj) m_j. Since m_k was not matched to μ(m_j) under μ it must be that μ(m_k) ≻_mk μ(m_j). Hence m_k ∈ B , otherwise μ(m_j) ≽ m_kν(m_k) ≽_mk μ(m_k) ≻_mk μ(m_j), which is a contradiction. Since m_k ∈ B and m_k has proposed to μ(m_j) at the time man m_j proposes, it means that m_k must have been rejected by μ(m_k) prior to m_j being rejected, contradicting our choice of m_j.

The mechanism associated with the male propose algorithm is not strategy-proof for the females. Let us see how this is the case by way of an example. The male propose algorithm returns the matching {(m₁, w₂), (m₂, w₃), (m₃, w₁)}. In the course of the algorithm the only woman who receives at least two proposals is w₁. She received proposals from m₂ and m₃. She rejects m₂ who goes on to propose to w₃ and the algorithm terminates. Notice that w₁ is matched with her second choice. Suppose now that she had rejected m₃ instead. Then m₃ would have gone on to proposes to w₂. Woman w₂ now has a choice between m₁ and m₃. She would keep m₃ and reject m₁, who would go on to propose to w₁. Woman w₁ would keep m₁ over m₂ and in the final matching be paired with a her first-rank choice.

Transposing this on to economic theory, this fits neatly into the Walrasian equilibrium. Walras’ law is an economic theory that the existence of excess supply in one market must be matched by excess demand in another market so that it balances out. Walras’ law asserts that an examined market must be in equilibrium if all other markets are in equilibrium, and this contrasts with Keynesianism, which by contrast, assumes that it is possible for just one market to be out of balance without a “matching” imbalance elsewhere. Moreover, Walrasian equilibria are the solutions of a fixed point problem. In the cases when they can be computed efficiently it is because the set of Walrasian equilibria can be described by a set of convex inequalities. The same can be said of stable matchings/marriages. The set of stable matchings is fixed points of a nondecreasing function defined on a lattice. 

AI learns to solve a Rubik’s Cube in 1.2 seconds

DeepCubeA uses a neural network (which apes how the human mind processes information) along with machine learning techniques, in which an AI system learns by detecting patterns and theorizing with little human input. It adopts a reinforcement learning approach, by which it learned “how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge.”

Endgadget

ABSTRACT

 

Recently, Approximate Policy Iteration (API) algorithms have achieved super- human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodi- dactic Iteration is able to learn how to solve the Rubik’s Cube without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.

 

SOLVING THE RUBIK’S CUBE WITH APPROXIMATE POLICY ITERATION

Homological Algebra – Does A∞ Algebra Compensate for any Loss of Information in the Study of Chain Complexes? 1.0

In an abelian category, homological algebra is the homotopy theory of chain complexes up to quasi-isomorphism of chain complexes.  When considering nonnegatively graded chain complexes, homological algebra may be viewed as a linearized version of the homotopy theory of homotopy types or infinite groupoids. When considering unbounded chain complexes, it may be viewed as a linearized and stabilized version. Conversely, we may view homotopical algebra as a nonabelian generalization of homological algebra.

Suppose we have a topological space X and a “multiplication map” m₂ : X × X → X. This map may or may not be associative; imposing associativity is an extra condition. An A_∞ space imposes a weaker structure, which requires m₂ to be associative up to homotopy, along with “higher order” versions of this. Indeed, there are very standard situations where one has natural multiplication maps which are not associative, but obey certain weaker conditions.

The standard example is when X is the loop space of another space M, i.e., if m₀ ∈ M is a chosen base point,

X = {x : [0,1] → M |x continuous, x(0) = x(1) = m₀}.

Composition of loops is then defined, with

x₂x₁(t) = x₂(2t), when 0 ≤ t ≤ 1/2

= x₁(2t−1), when  1/2 ≤ t ≤ 1

However, this composition is not associative, but x₃(x₂x₁) and (x₁x₂)x₃ are homotopic loops.

On the left, we first traverse x₃ from time 0 to time 1/2, then traverse x₂ from time 1/2 to time 3/4, and then x₁ from time 3/4 to time 1. On the right, we first traverse x₃ from time 0 to time 1/4, x₂ from time 1/4 to time 1/2, and then x₁ from time 1/2 to time 1. By continuously deforming these times, we can homotop one of the loops to the other. This homotopy can be represented by a map

m₃ : [0, 1] × X × X × X → X such that

{0} × X × X × X → X is given by (x₃, x₂, x₁) 􏰀→ m₂(x₃, m₂(x₂, x₁)) and

{1} × X × X × X → X is given by (x₃, x₂, x₁) 􏰀→ m₂(m₂(x₃, x₂), x₁)

What, if we have four elements x₁, . . . , x₄ of X? Then there are a number of different ways of putting brackets in their product, and these are related by the homotopies defined by m₃. Indeed, we can relate

((x₄x₃)x₂)x₁ and x₄(x₃(x₂x₁))

in two different ways:

((x₄x₃)x₂)x₁ ∼ (x₄x₃)(x₂x₁) ∼ x₄(x₃(x₂x₁))

and

((x₄x₃)x₂)x₁ ∼ (x₄(x₃x₂))x₁ ∼ x₄((x₃x₂)x₁) ∼ x₄(x₃(x₂x₁)).

Here each ∼ represents a homotopy given by m₃.

Schematically, this is represented by a polygon, S₄, with each vertex labelled by one of the ways of associating x₄x₃x₂x₁, and the edges represent homotopies between them

The homotopies m₃ yield a map ∂S₄ × X₄ → X which is defined using appropriate combinations of m₂ and m₃ on each edge of the boundary of S₄. For example, restricting to the edge with vertices ((x₄x₃)x₂)x₁ and (x₄(x₃x₂))x₁, this map is given by (s, x₄, . . . , x₁) 􏰀→ m₂(m₃(s, x₄, x₃, x₂), x₁).

Thus the conditionality on the structure becomes: this map extend across S₄, giving a map

m₄ : S₄ × X₄ → X.

As homological algebra seeks to study complexes by taking quotient modules to obtain the homology, the question arises as to whether any information is lost in this process. This is equivalent to asking whether it is possible to reconstruct the original complex (up to quasi-isomorphism) given its homology or whether some additional structure is needed in order to be able to do this. The additional structure that is needed is an A_∞-structure constructed on the homology of the complex…

 

From God’s Perspective, There Are No Fields…Justified Newtonian, Unjustified Relativistic Claim. Note Quote.

Electromagnetism is a relativistic theory. Indeed, it had been relativistic, or Lorentz invariant, before Einstein and Minkowski understood that this somewhat peculiar symmetry of Maxwell’s equations was not accidental but expressive of a radically new structure of time and space. Minkowski spacetime, in contrast to Newtonian spacetime, doesn’t come with a preferred space-like foliation, its geometric structure is not one of ordered slices representing “objective” hyperplanes of absolute simultaneity. But Minkowski spacetime does have an objective (geometric) structure of light-cones, with one double-light-cone originating in every point. The most natural way to define a particle interaction in Minkowski spacetime is to have the particles interact directly, not along equal-time hyperplanes but along light-cones

In other words, if z_i(τ_􏱁i)  and z_j(τ_j􏱁) denote the trajectories of two charged particles, it wouldn’t make sense to say that the particles interact at “equal times” as it is in Newtonian theory. It would however make perfectly sense to say that the particles interact whenever

(z^μ_i z^μ_j)(z^μ_i z^μ_j) = (z_i – z_j)² = 0 —– (1)

For an observer finding himself in a universe guided by such laws it might then seem like the effects of particle interactions were propagating through space with the speed of light. And this observer may thus insist that there must be something in addition to the particles, something moving or evolving in spacetime and mediating interactions between charged particles. And all this would be a completely legitimate way of speaking, only that it would reflect more about how things appear from a local perspective in a particular frame of reference than about what is truly and objectively going on in the physical world. From “Gods perspective” there are no fields (or photons, or anything of that kind) – only particles in spacetime interacting with each other. This might sound hypothetical, but, it actually is not entirely fictitious. for such a formulation of electrodynamics actually exists and is known as Wheeler-Feynman electrodynamics, or Wheeler-Feynman Absorber Theory. There is a formal property of field equations called “gauge invariance” which makes it possible to look at things in several different, but equivalent, ways. Because of gauge invariance, this theory says that when you push on something, it creates a disturbance in the gravitational field that propagates outward into the future. Out there in the distant future the disturbance interacts with chiefly the distant matter in the universe. It wiggles. When it wiggles it sends a gravitational disturbance backward in time (a so-called “advanced” wave). The effect of all of these “advanced” disturbances propagating backward in time is to create the inertial reaction force you experience at the instant you start to push (and cancel the advanced wave that would otherwise be created by you pushing on the object). So, in this view fields do not have a real existence independent of the sources that emit and absorb them. It is defined by the principle of least action.

Wheeler–Feynman electrodynamics and Maxwell–Lorentz electrodynamics are for all practical purposes empirically equivalent, and it may seem that the choice between the two candidate theories is merely one of convenience and philosophical preference. But this is not really the case since the sad truth is that the field theory, despite its phenomenal success in practical applications and the crucial role it played in the development of modern physics, is inconsistent. The reason is quite simple. The Maxwell–Lorentz theory for a system of N charged particles is defined, as it should be, by a set of mathematical equations. The equation of motion for the particles is given by the Lorentz force law, which is

The electromagnetic force F on a test charge at a given point and time is a certain function of its charge q and velocity v, which can be parameterized by exactly two vectors E and B, in the functional form:

describing the acceleration of a charged particle in an electromagnetic field. The electromagnetic field, represented by the field-tensor F^μν, is described by Maxwell’s equations. The homogenous Maxwell equations tell us that the antisymmetric tensor F^μν (a 2-form) can be written as the exterior derivative of a potential (a 1-form) A^μ(x), i.e. as

F^μν = ∂^μ A^ν – ∂^ν A^μ —– (2)

The inhomogeneous Maxwell equations couple the field degrees of freedom to matter, that is, they tell us how the charges determine the configuration of the electromagnetic field. Fixing the gauge-freedom contained in (2) by demanding ∂_μA^μ(x) = 0 (Lorentz gauge), the remaining Maxwell equations take the particularly simple form:

□ A^μ = – 4π j^μ —– (3)

where

□ = ∂_μ∂^μ

is the d’Alembert operator and j^μ the 4-current density.

The light-cone structure of relativistic spacetime is reflected in the Lorentz-invariant equation (3). The Liénard–Wiechert field at spacetime point x depends on the trajectories of the particles at the points of intersection with the (past and future) light-cones originating in x. The Liénard–Wiechert field (the solution of (3)) is singular precisely at the points where it is needed, namely on the world-lines of the particles. This is the notorious problem of the electron self-interaction: a charged particle generates a field, the field acts back on the particle, the field-strength becomes infinite at the point of the particle and the interaction terms blow up. Hence, the simple truth is that the field concept for managing interactions between point-particles doesn’t work, unless one relies on formal manipulations like renormalization or modifies Maxwell’s laws on small scales. However, we don’t need the fields and by taking the idea of a relativistic interaction theory seriously, we can “cut the middle man” and let the particles interact directly. The status of the Maxwell equation’s (3) in Wheeler–Feynman theory is now somewhat analogous to the status of Laplace’s equation in Newtonian gravity. We can get to the Gallilean invariant theory by writing the force as the gradient of a potential and having that potential satisfy the simplest nontrivial Galilean invariant equation, which is the Laplace equation:

∆V(x, t) = ∑_iδ(x – x_i(t)) —– (4)

Similarly, we can get the (arguably) simplest Lorentz invariant theory by writing the force as the exterior derivative of a potential and having that potential satisfy the simplest nontrivial Lorentz invariant equation, which is (3). And as concerns the equation of motion for the particles, the trajectories, if, are parametrized by proper time, then the Minkowski norm of the 4-velocity is a constant of motion. In Newtonian gravity, we can make sense of the gravitational potential at any point in space by conceiving its effect on a hypothetical test particle, feeling the gravitational force without gravitating itself. However, nothing in the theory suggests that we should take the potential seriously in that way and conceive of it as a physical field. Indeed, the gravitational potential is really a function on configuration space rather than a function on physical space, and it is really a useful mathematical tool rather than corresponding to physical degrees of freedom. From the point of view of a direct interaction theory, an analogous reasoning would apply in the relativistic context. It may seem (and historically it has certainly been the usual understanding) that (3), in contrast to (4), is a dynamical equation, describing the temporal evolution of something. However, from a relativistic perspective, this conclusion seems unjustified.

Conformal Field Theory and Virasoro Algebra. Note Quote.

There are a few reasons why Conformal Field Theories (CFTs) are very interesting to study: The first is that at fixed points of Renormalization Group flows, or at second order phase transitions, a quantum field theory is scale invariant. Scale invariance is a weaker form of conformal invariance, and it turns out in all cases that we know of scale invariance of a quantum field theory actually ends up implying the larger symmetry of conformal invariance. The second reason is that the requirement that a theory is conformally invariant is so restrictive that many things can be solved for that would otherwise be intractable. As an example, conformal invariance fixes 2- and 3-point functions entirely. In an ordinary quantum field theory, especially one at strong coupling, these would be hard or impossible to calculate at all. A third reason is string theory. In string theory, the worldsheet theory describing the string’s excitations is a CFT, so if string theory is correct, then in some sense conformal invariance is really one of the most fundamental features of the elemental constituents of reality. And through string theory we have the most precise and best-understood gauge/gravity dualities (the AdS/CFT dualities) that also involve CFT’s.

A Conformal Field Theory (CFT) is a Quantum Field Theory (QFT) in which conformal rescaling of the metric acts by conjugation. For the family of morphisms D_g, 

D_[e^hg] = e^c·α[h] L⁻¹[h|_B1] D_g L[h|_B2] —– (1)

The analogous statement (conjugating the state on each boundary) is true for any Σ.

Here L is a linear operator depending only on the restriction of h to one of the boundaries of the annulus. All the dependence on the conformal rescaling away from the boundary is determined by a universal (independent of the particular Conformal Field Theory) functional α[h] ∈ R, which appears in an overall multiplicative factor e^c·α[h]. The quantity c, called “Virasoro central charge”.

The corresponding operators L[h] form a semigroup, with a self-adjoint generator H. Then, since according to the axioms of QFT the spectrum of H is bounded below, we can promote this to a group action. This can be used to map any of the Hilbert spaces H_d to a single H_l for a fixed value of l, say l = 1. We will now do this and use the simpler notation H ≅ H₁,

How do we determine the L[h]? First, we uniformize Σ – in other words, we find a complex diffeomorphism φ from our surface with boundary Σ to a constant curvature surface. We then consider the restriction of φ to each of the boundary components B_i, to get an element φ_i of Diff S¹ × R⁺, where the R⁺ factor acts by an overall rescaling. We then express each φ_i as the exponential of an element l_i in the Lie algebra Diff S¹, to find an appropriate projective representation of this Lie algebra on H.

Certain subtleties are in order here: The Lie algebra Diff S¹ which appears is actually a subalgebra of a direct sum of two commuting algebras, which act independently on “left moving” and “right moving” factors in H. Thus, we can write H as a direct sum of irreps of this direct sum algebra,

H = ⊕_iH_L,i ⊗ H_R,i —– (2)

Each of these two commuting algebras is a central extension of the Lie algebra Diff S¹, usually called the Virasoro algebra or Vir.

Now, consider the natural action of Diff S¹ on functions on an S¹ parameterized by θ ∈ [0, 2π). After complexification, we can take the following set of generators,

l_n = −ie^inθ ∂/∂θ n ∈ Z —– (3)

which satisfy the relations

[l_m, l_n] = (m − n)l_m+n —– (4)

The Virasoro algebra is the universal central extension of this, with generators L_n with n ∈ Z, c ∈ R, and the relations

[L_m, L_n] = (m − n)L_m+n + c/12 n(n² − 1)δ_m+n,0 —– (5)

The parameter c is again the Virasoro central charge. It is to be noted that the central extension is required in any non-trivial unitary CFT. Unitarity and other QFT axioms require the Virasoro representation to act on a Hilbert space, so that L_−n = L^†_n. In particular, L₀ is self-adjoint and can be diagonalized. Take a “highest weight representation,” in which the spectrum of L₀ is bounded below. The L₀ eigenvector with the minimum eigenvalue, h, is by definition the “highest weight state”, or a state |h⟩, so that

L₀|h⟩ = h|h⟩ —– (6)

and normalize it so that ⟨h|h⟩ = 1. Since this is a norm in a Hilbert space, we conclude that h ≥ 0, with equality only if L₋₁|h⟩ = 0. In fact, L₋₁|0⟩ = 0 can be related to the translation invariance of the vacuum. Rephrasing this in terms of local operators, instead of in terms of states, take Σ to be the infinite cylinder R × S¹, or equivalently the punctured complex plane C^∗ with the complex coordinate z. In a CFT the component T_zz of the stress tensor can be expressed in terms of the Virasoro generators:

T_zz ≡ T(z) = ∑_n∈Z L_nz⁻ⁿ⁻² —– (7)

The component T_z̄z̄ is antiholomorphic and can be similarly expressed in terms of the generators L̄_n of the second copy of the Virasoro algebra:

T_z̄z̄ ≡ T(z̄) = ∑_n∈Z L̄_nz̄⁻ⁿ⁻² —– (8)

The mixed component T_zz̄ = T_z̄z is a c-number which vanishes for a flat metric. The state corresponding to T(z) is L₋₂|0⟩.

Define Operators Corresponding to Cobordisms Only Iff Each Connected Component of the Cobordism has Non-empty Outgoing Boundary. Drunken Risibility.

 

Define a category B whose objects are the oriented submanifolds of X, and whose vector space of morphisms from Y to Z is O_YZ = Ext^∗_H^∗(X)(H^∗(Y), H^∗(Z)) – the cohomology, as usual, has complex coefficients, and H^∗(Y) and H^∗(Z) are regarded as H^∗(X)-modules by restriction. The composition of morphisms is given by the Yoneda composition of Ext groups. With this definition, however, it will not be true that O_YZ is dual to O_ZY. (To see this it is enough to consider the case when Y = Z is a point of X, and X is a product of odd-dimensional spheres; then O_YZ is a symmetric algebra, and is not self-dual as a vector space.)

We can do better by defining a cochain complex O’_YZ of morphisms by

O’_YZ = B_Ω(X)(Ω(Y), Ω(Z)) —– (1)

where Ω(X) denotes the usual de Rham complex of a manifold X, and B_A(B,C), for a differential graded algebra A and differential graded A- modules B and C, is the usual cobar resolution

Hom(B, C) → Hom(A ⊗ B, C) → Hom(A ⊗ A ⊗ B, C) → · · ·  —– (2)

in which the differential is given by

dƒ(a₁ ⊗ · · · ⊗ a_k ⊗ b) = 􏰝a₁ ƒ(a₂ ⊗ · · · ⊗ a_k ⊗ b) + ∑(-1)ⁱ ƒ(a₁ ⊗ · · · ⊗ a_ia_i+1 ⊗ a_k ⊗ b) + (-1)^k ƒ(a₁ ⊗ · · · ⊗ a_k-1 ⊗ a_kb) —– (3)

whose cohomology is Ext_A(B,C). This is different from O_YZ = Ext^∗_H^∗(X)(H^∗(Y), H^∗(Z)), but related to it by a spectral sequence whose E₂-term is O_YZ and which converges to H^∗(O’_YZ) = Ext_Ω(X)(Ω(Y), Ω(Z)). But more important is that H^∗(O’_YZ) is the homology of the space P_YZ of paths in X which begin in Y and end in Z. To be precise, H_p(O’_YZ) ≅ H_p+dZ(P_YZ), where dZ is the dimension of Z. On the cochain complexes the Yoneda composition is associative up to cochain homotopy, and defines a structure of an A_∞ category B’. The corresponding composition of homology groups

H_i(P_YZ) × H_j(P_ZW) → H_i+j−dZ(P_YW) —— (4)

is the composition of the Gysin map associated to the inclusion of the codimension dZ submanifold M of pairs of composable paths in the product P_YZ × P_ZW with the concatenation map M → P_YW.

Now let’s attempt to fit the closed string cochain algebra C to this A_∞ category. C is equivalent to the usual Hochschild complex of the differential graded algebra Ω(X), whose cohomology is the homology of the free loop space LX with its degrees shifted downwards by the dimension d_X of X, so that the cohomology H_i(C) is potentially non-zero for −d_X ≤ i < ∞. There is a map H_i(X) → H_−i(C) which embeds the ordinary cohomology ring of X to the Pontrjagin ring of the based loop space L₀X, based at any chosen point in X.

The structure is, however, not a cochain-level open and closed theory, as we have no trace maps inducing inner products on H^∗(O’_YZ). When one tries to define operators corresponding to cobordisms it turns out to be possible only when each connected component of the cobordism has non-empty outgoing boundary. 

The Mathematics of Political Policies and Economic Decisions – Dictatorial (Extremists) Versus Democratic. Note Quote. Part 1 Didactics.

If a strategy-proof, onto rule does not pick x∗ when it is the median of peaks and y_m‘s, then a contradiction is reached using preferences with peaks at p_i^L and p_i^H.

Let us take a look at single-peaked preferences over one-dimensional policy spaces. This domain can be used to model political policies, economic decisions, location problems, or any allocation problem where a single point must be chosen in an interval. The key assumption is that agents’ preferences are assumed to have a single most-preferred point in the interval, and that preferences are “decreasing” as one moves away from that peak.

Formally, the allocation space (or policy space) is the unit interval A = [0, 1]. An outcome is a single point x ∈ A. Each agent i ∈ N has a preference ordering ≽_i, which is a weak order over the outcomes in [0, 1]. The preference relation ≽_i is single-peaked if ∃ a point p_i ∈ A (the peak of ≽_i) such that ∀ x ∈ A\{p_i} and all λ ∈ [0,1), (λx +(1−λ)p_i) ≻_i x. Let R denote the class of single-peaked preferences.

We denote the peaks of preference relations ≽_i, ≽′_i, ≽_j, etc., respectively by p_i, p_i′, p_j, etc. Denote a profile (n-tuple) of preferences as ≽ ∈ Rⁿ.

One can imagine this model as representing a political decision such as an income tax rate, another political issue with conservative/liberal extremes, the location of a public facility on a road, or even something as simple as a group of people deciding on the temperature setting for a shared office. Here, the agents have an ideal preferred policy in mind, and would prefer that a decision be made as close as possible to this “peak.”

A rule f : Rⁿ → A assigns an outcome f(≽) to any preference profile ≽. A rule is strategy-proof if it is a dominant strategy for each agent to report his preferences truthfully when the rule is being used to choose a point.

Our purpose then is to see that this class of problems admits a rich family of strategy-proof rules whose ranges include more than two alternatives. In fact, the family of such rules remains rich even when one restricts attention to rules that satisfy the following condition.

We say that a rule f is onto if ∀ x ∈ A ∃ ≽ ∈ Rⁿ such that f(≽) = x. An onto rule cannot preclude an outcome from being chosen ex ante. It is not without loss of generality to impose this condition. For instance, fix two points x, y ∈ [0, 1] and consider a rule that chooses whichever of the two points is preferred to the other by a majority of agents (and where x is chosen in case of a tie). Such a rule is strategy-proof, but not onto. Similar strategy-proof rules can even break ties between x and y by using preference information about other points x′, y′, . . ., in [0, 1], even though x′, etc., are not in the range of the rule.

The onto condition is even weaker than what is called unanimity, which requires that whenever all agents’ preferences have the same peak (p_i = p_j ∀ i, j), the rule must choose that location as the outcome. In turn, unanimity is weaker than Pareto-optimality: ∀ ≽ ∈ Rⁿ, ∃ no point x ∈ [0, 1] such that x ≽_i f(≽) ∀ i ∈ N.

As it turns out, these three requirements are all equivalent among strategy-proof rules. Suppose f is strategy-proof. Then f is onto iff it is unanimous iff it is Pareto-optimal.

It is clear that Pareto-optimality implies the other two conditions. Suppose f is strategy-proof and onto. Fix x ∈ [0, 1] and let ≽ ∈ Rⁿ be such that f(≽) = x. Consider any “unanimous” profile ≽′ ∈ Rⁿ such that p_i′ = x for each i ∈ N. By strategy-proofness, f (≽′₁, ≽₂, . . . , ≽_n) = x, otherwise agent 1 could manipulate f. Repeating this argument, f (≽′₁, ≽′₂, ≽₃, . . . , ≽_n) = x, . . . ,f(≽′) = x. That is, f is unanimous.

In order to derive a contradiction, suppose that f is not Pareto-optimal at some profile ≽ ∈ Rⁿ. This implies that either (i) f(≽) < p_i ∀ i ∈ N or (ii) f(≽) > p_i ∀ i ∈ N . Without loss of generality, assume (i) holds. Furthermore, assume that the agents are labeled so that p₁ ≤ p₂ ≤ ··· ≤ p_n.

If p₁ = p_n then unanimity is violated, completing the proof. Otherwise, let j ∈ N be such that p₁ = p_j < p_j+1; that is, j < n agents have the minimum peak. ∀ i > j, let ≽′_i be a preference relation such that both p_i′ = p₁ and f(≽)≽′_i p_i.

Let x_n = f(≽₁,…, ≽_n−1, ≽′_n). By strategy-proofness, x_n ∈ [f(≽), p_n], otherwise agent n (with preference ≽′_n) could manipulate f by reporting preference ≽_n. Similarly, x_n ∉ (f(≽), p_n], otherwise agent n (with preference ≽_n) could manipulate f by reporting preference ≽′_n. Therefore x_n = f(≽).

Repeating this argument as each i > j replaces ≽_i with ≽′_i, we have f(≽₁,…, ≽_j, ≽′_j+1,…, ≽′_n) = f(≽), which contradicts unanimity. Since a strategy-proof, onto rule must be unanimous, this is a contradiction.

The central strategy-proof rule on this domain is the simple median-voter rule. Suppose that the number of agents n is odd. Then the rule that picks the median of the agents’ peaks (p_i ’s) is a strategy-proof rule.

It is easy to see why this rule is strategy-proof : If an agent’s peak p_i lies below the median peak, then he can change the median only by reporting a preference relation whose peak lies above the true median. The effect of this misreport is for the rule to choose a point even further away from p_i, making the agent worse off. A symmetric argument handles the case in which the peak is above the median. Finally, an agent cannot profitably misreport his preferences if his peak is the median one to begin with.

More generally, for any number of agents n and any positive integer k ≤ n, the rule that picks the k^th highest peak is strategy-proof for precisely the same reasons. An agent can only move the k^th peak further from his own. The median happens to be the case where k = (n + 1)/2.

The strategy-proofness of such rules stands in contrast to the incentives properties of rules that choose average-type statistics. Consider the rule that chooses the average of the n agents’ peaks. Any agent with peak p_i ∈ (0, 1) that is not equal to the average can manipulate the rule by reporting preferences with a more extreme peak (closer to 0 or 1) than his true peak.

This would also hold for any weighted average of the agents’ peaks, with one exception. If a rule allocated all of the weight to one agent, then the resulting rule simply picks that agent’s peak always. Such a dictatorial rule is strategy-proof and onto.

In addition to favorable incentives properties, rules based on order statistics have the property that they require little information to be computed. Technically a rule requires agents to report an entire preference ordering over [0, 1]. There is a need to transcend the rules, which, only require agents to report their most preferred point, i.e., a single number. In fact, under the onto assumption, this informational property is a consequence of the strategy-proofness requirement; that is, all strategy-proof and onto rules have the property that they can be computed solely from information about the agents’ peaks.

Let us generalize the class of “k^th-statistic rules”. Consider a fixed set of points y₁, y₂, . . . , y_n−1 ∈ A. Consider the rule that, for any profile of preferences ≽, chooses the median of the 2n − 1 points consisting of the n agents’ peaks and the n − 1 points of y. This differs in that, for some choices of y and some profiles of preferences, the rule may choose a point that is not the peak of any agent’s preferences. Yet, such a rule is strategy-proof.

Such rules compose the entire class of strategy-proof and onto rules that treat agents symmetrically. To formalize this latter requirement, we call a rule anonymous if for any ≽ ∈ Rⁿ and any permutation ≽′ of ≽, f(≽′) = f (≽). This requirement captures the idea that the agents’ names play no role in the behavior of a rule. Dictatorial rules are examples of rules that are strategy-proofand onto, but not anonymous.

A rule f is strategy-proof, onto, and anonymous iff ∃ y₁, y₂,…, y_n−1 ∈ [0,1] such that ∀ ≽ ∈ Rⁿ,

f(≽) = med{p₁, p₂,…, p_n, y₁, y₂,…, y_n−1} —– (1)

Suppose f is strategy-proof, onto, and anonymous. We make extensive use of the two (extreme) preference relations that have peaks at 0 and 1 respectively. Since preferences relations are ordinal, there is only one preference relation with a peak at 0 and only one with a peak at 1. Denote these two preference relations by ≽⁰_i and ≽¹_i respectively.

For any 1 ≤ m ≤ n − 1, let y_m denote the outcome of f when m agents have preference relation ≽¹_i and the remainder have ≽⁰_i:

y_m = f(≽⁰₁,…, ≽⁰_n−m, ≽¹_n−m+1,…, ≽¹_n)

By anonymity, the order of the arguments of f is irrelevant; if precisely m agents have preference relation ≽¹_i and the rest have ≽⁰_i then the outcome is y_m.

With a profile of preferences ≽ ∈ Rⁿ with peaks p₁, . . ., p_n, and without loss of generality (by anonymity), once one assumes that p_i ≤ p_i+1 for each i ≤ n−1, then,

f(≽) = x∗ ≡ med{p₁,…, p_n, y₁,…, y_n−1}.

If the median is some y_m, then suppose x∗ = y_m for some m. By monotonicity of the peaks and y_m‘s, since x∗ is the median of 2n−1 points this implies p_n−m ≤ x∗ = y_m ≤ p_n−m+1. By assumption,

x∗ = y_m = f(≽⁰₁,…, ≽⁰_n−m, ≽¹_n−m+1,…, ≽¹_n) —– (2)

Let x₁ = f(≽₁, ≽⁰₂,…, ≽⁰_n−m, ≽¹_n−m+1,…, ≽¹_n). Strategy-proofness implies x₁ ≥ x∗, otherwise agent 1 with preference ≽⁰₁ could manipulate f. Similarly, since p₁ ≤ y_m, we cannot have x₁ > x∗, otherwise agent 1 with preference ≽₁ could manipulate f. Hence x₁ = x∗. Repeating this argument for all i ≤ n − m, x∗ = f(≽₁,…,≽_n−m, ≽¹_n−m+1,…, ≽¹_n). The symmetric argument for all i > n−m implies

f(≽₁,…, ≽_n) = x∗ —– (3)

If the median is an agent’s peak, then the remaining case is that y_m < x∗ < y_m+1 for some m. (The cases where x∗ < y₁ and x∗ > y_n−1 are similar, denoting y₀ = 0 and y_n = 1). In this case, since the agents’ peaks are in increasing order, we have x∗ = p_n−m. If

f(≽⁰₁,…, ≽⁰_n−m−1, ≽_n−m, ≽¹_n−m+1,…, ≽¹_n) = x∗ = p_n−m —– (4)

then, analogous to the way (2) implied (3), repeated applications of strategy-proofness (to the n−1 agents other than i = n−m) would imply f(≽₁,…, ≽_n) = x∗, and the proof would be finished.

Thus, the parameters (y_m‘s) can be thought of as the rule’s degree of compromise when agents have extremist preferences. If m agents prefer the highest possible outcome (1), while n − m prefer the lowest (0), then which point should be chosen? A true median rule would pick whichever extreme (0 or 1) contains the most peaks. On the other hand, the other rules may choose intermediate points (y_m) as a compromise. The degree of compromise (which y_m) can depend on the degree to which the agents’ opinions are divided (the size of m).

The anonymity requirement is a natural one in situations where agents are to be treated as equals. If one does not require this, however, the class of strategy-proof rules becomes even larger. Along the dictatorial rules, which always chooses a predetermined agent’s peak, there are less extreme violations of anonymity: The full class of strategy-proof, onto rules, allows agents to be treated with varying degrees of asymmetry.