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Stackelberg Equilibrium

The Stackelberg Equilibrium is a concept in game theory that describes a strategic interaction between firms in an oligopoly setting, where one firm (the leader) makes its production decision before the other firm (the follower). This sequential decision-making process allows the leader to optimize its output based on the expected reactions of the follower. In this equilibrium, the leader anticipates the follower's best response and chooses its output level accordingly, leading to a distinct outcome compared to simultaneous-move games.

Mathematically, if qLq_LqL​ represents the output of the leader and qFq_FqF​ represents the output of the follower, the follower's reaction function can be expressed as qF=R(qL)q_F = R(q_L)qF​=R(qL​), where RRR is the reaction function derived from the follower's profit maximization. The Stackelberg equilibrium occurs when the leader chooses qLq_LqL​ that maximizes its profit, taking into account the follower's reaction. This results in a unique equilibrium where both firms' outputs are determined, and typically, the leader enjoys a higher market share and profits compared to the follower.

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Dirac Equation

The Dirac Equation is a fundamental equation in quantum mechanics and quantum field theory, formulated by physicist Paul Dirac in 1928. It describes the behavior of fermions, which are particles with half-integer spin, such as electrons. The equation elegantly combines quantum mechanics and special relativity, providing a framework for understanding particles that exhibit both wave-like and particle-like properties. Mathematically, it is expressed as:

(iγμ∂μ−m)ψ=0(i \gamma^\mu \partial_\mu - m) \psi = 0(iγμ∂μ​−m)ψ=0

where γμ\gamma^\muγμ are the Dirac matrices, ∂μ\partial_\mu∂μ​ is the four-gradient operator, mmm is the mass of the particle, and ψ\psiψ is the wave function representing the particle's state. One of the most significant implications of the Dirac Equation is the prediction of antimatter; it implies the existence of particles with the same mass as electrons but opposite charge, leading to the discovery of positrons. The equation has profoundly influenced modern physics, paving the way for quantum electrodynamics and the Standard Model of particle physics.

Runge’S Approximation Theorem

Runge's Approximation Theorem ist ein bedeutendes Resultat in der Funktionalanalysis und der Approximationstheorie, das sich mit der Approximation von Funktionen durch rationale Funktionen beschäftigt. Der Kern des Theorems besagt, dass jede stetige Funktion auf einem kompakten Intervall durch rationale Funktionen beliebig genau approximiert werden kann, vorausgesetzt, dass die Approximation in einem kompakten Teilbereich des Intervalls erfolgt. Dies wird häufig durch die Verwendung von Runge-Polynomen erreicht, die eine spezielle Form von rationalen Funktionen sind.

Ein wichtiger Aspekt des Theorems ist die Identifikation von Rationalen Funktionen als eine geeignete Klasse von Funktionen, die eine breite Anwendbarkeit in der Approximationstheorie haben. Wenn beispielsweise fff eine stetige Funktion auf einem kompakten Intervall [a,b][a, b][a,b] ist, gibt es für jede positive Zahl ϵ\epsilonϵ eine rationale Funktion R(x)R(x)R(x), sodass:

∣f(x)−R(x)∣<ϵfu¨r alle x∈[a,b]|f(x) - R(x)| < \epsilon \quad \text{für alle } x \in [a, b]∣f(x)−R(x)∣<ϵfu¨r alle x∈[a,b]

Dies zeigt die Stärke von Runge's Theorem in der Approximationstheorie und seine Relevanz in verschiedenen Bereichen wie der Numerik und Signalverarbeitung.

Bargaining Nash

The Bargaining Nash solution, derived from Nash's bargaining theory, is a fundamental concept in cooperative game theory that deals with the negotiation process between two or more parties. It provides a method for determining how to divide a surplus or benefit based on certain fairness axioms. The solution is characterized by two key properties: efficiency, meaning that the agreement maximizes the total benefit available to the parties, and symmetry, which ensures that if the parties are identical, they should receive identical outcomes.

Mathematically, if we denote the utility levels of parties as u1u_1u1​ and u2u_2u2​, the Nash solution can be expressed as maximizing the product of their utilities above their disagreement points d1d_1d1​ and d2d_2d2​:

max⁡(u1,u2)(u1−d1)(u2−d2)\max_{(u_1, u_2)} (u_1 - d_1)(u_2 - d_2)(u1​,u2​)max​(u1​−d1​)(u2​−d2​)

This framework allows for the consideration of various negotiation factors, including the parties' alternatives and the inherent fairness in the distribution of resources. The Nash bargaining solution is widely applicable in economics, political science, and any situation where cooperative negotiations are essential.

Lempel-Ziv Compression

Lempel-Ziv Compression, oft einfach als LZ bezeichnet, ist ein verlustfreies Komprimierungsverfahren, das auf der Identifikation und Codierung von wiederkehrenden Mustern in Daten basiert. Die bekanntesten Varianten sind LZ77 und LZ78, die beide eine effiziente Methode zur Reduzierung der Datenmenge bieten, indem sie redundante Informationen eliminieren.

Das Grundprinzip besteht darin, dass die Algorithmen eine dynamische Tabelle oder ein Wörterbuch verwenden, um bereits verarbeitete Daten zu speichern. Wenn ein Wiederholungsmuster erkannt wird, wird stattdessen ein Verweis auf die Position und die Länge des Musters in der Tabelle gespeichert. Dies kann durch die Erzeugung von Codes erfolgen, die sowohl die Position als auch die Länge des wiederkehrenden Musters angeben, was üblicherweise in der Form (p,l)(p, l)(p,l) dargestellt wird, wobei ppp die Position und lll die Länge ist.

Lempel-Ziv Compression ist besonders in der Datenübertragung und -speicherung nützlich, da sie die Effizienz erhöht und Speicherplatz spart, ohne dass Informationen verloren gehen.

Anisotropic Etching

Anisotropic etching is a specialized technique used in semiconductor manufacturing and microfabrication that selectively removes material from a substrate in a specific direction. This process is crucial for creating well-defined features with high aspect ratios, which means deep structures in relation to their width. Unlike isotropic etching, where material is removed uniformly in all directions, anisotropic etching allows for greater control and precision, resulting in vertical sidewalls and sharp corners.

This technique can be achieved using various methods, including wet etching with specific chemicals or dry etching techniques such as Reactive Ion Etching (RIE). The choice of method affects the etching profile and the materials that can be effectively used. Anisotropic etching is widely employed in the fabrication of microelectronic devices, MEMS (Micro-Electro-Mechanical Systems), and nanostructures, making it a vital process in modern technology.

Semiconductor Doping Concentration

Semiconductor doping concentration refers to the amount of impurity atoms introduced into a semiconductor material to modify its electrical properties. By adding specific atoms, known as dopants, to intrinsic semiconductors (like silicon), we can create n-type or p-type semiconductors, which have an excess of electrons or holes, respectively. The doping concentration is typically measured in atoms per cubic centimeter (atoms/cm³) and plays a crucial role in determining the conductivity and overall performance of the semiconductor device.

For example, a higher doping concentration increases the number of charge carriers available for conduction, enhancing the material's electrical conductivity. However, excessive doping can lead to reduced mobility of charge carriers due to increased scattering, which can adversely affect device performance. Thus, optimizing doping concentration is essential for the design of efficient electronic components such as transistors and diodes.