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Shapley Value

The Shapley Value is a solution concept in cooperative game theory that assigns a unique distribution of a total surplus generated by a coalition of players. It is based on the idea of fairly allocating the gains from cooperation among all participants, taking into account their individual contributions to the overall outcome. The Shapley Value is calculated by considering all possible permutations of players and determining the marginal contribution of each player as they join the coalition. Formally, for a player iii, the Shapley Value ϕi\phi_iϕi​ can be expressed as:

ϕi(v)=∑S⊆N∖{i}∣S∣!⋅(∣N∣−∣S∣−1)!∣N∣!⋅(v(S∪{i})−v(S))\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{|S|! \cdot (|N| - |S| - 1)!}{|N|!} \cdot (v(S \cup \{i\}) - v(S))ϕi​(v)=S⊆N∖{i}∑​∣N∣!∣S∣!⋅(∣N∣−∣S∣−1)!​⋅(v(S∪{i})−v(S))

where NNN is the set of all players, SSS is a subset of players not including iii, and v(S)v(S)v(S) represents the value generated by the coalition SSS. The Shapley Value ensures that players who contribute more to the success of the coalition receive a larger share of the total payoff, promoting fairness and incentivizing cooperation among participants.

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Ternary Search

Ternary Search is an efficient algorithm used for finding the maximum or minimum of a unimodal function, which is a function that increases and then decreases (or vice versa). Unlike binary search, which divides the search space into two halves, ternary search divides it into three parts. Given a unimodal function f(x)f(x)f(x), the algorithm consists of evaluating the function at two points, m1m_1m1​ and m2m_2m2​, which are calculated as follows:

m1=l+(r−l)3m_1 = l + \frac{(r - l)}{3}m1​=l+3(r−l)​ m2=r−(r−l)3m_2 = r - \frac{(r - l)}{3}m2​=r−3(r−l)​

where lll and rrr are the current bounds of the search space. Depending on the values of f(m1)f(m_1)f(m1​) and f(m2)f(m_2)f(m2​), the algorithm discards one of the three segments, thereby narrowing down the search space. This process is repeated until the search space is sufficiently small, allowing for an efficient convergence to the optimum point. The time complexity of ternary search is generally O(log⁡3n)O(\log_3 n)O(log3​n), making it a useful alternative to binary search in specific scenarios involving unimodal functions.

Foreign Reserves

Foreign reserves refer to the assets held by a country's central bank or monetary authority in foreign currencies. These reserves are essential for managing a nation's exchange rate and ensuring financial stability. Typically, foreign reserves consist of foreign currencies, gold, and special drawing rights (SDRs) from the International Monetary Fund (IMF).

The primary purposes of maintaining foreign reserves include:

  • Facilitating international trade by enabling the country to pay for imports.
  • Supporting the national currency in case of volatility in the foreign exchange market.
  • Acting as a buffer against economic shocks, allowing a government to stabilize its economy during times of crisis.

Foreign reserves are a critical indicator of a country's economic health and its ability to repay international debts.

Electron Beam Lithography

Electron Beam Lithography (EBL) is a sophisticated technique used to create extremely fine patterns on a substrate, primarily in semiconductor manufacturing and nanotechnology. This process involves the use of a focused beam of electrons to expose a specially coated surface known as a resist. The exposed areas undergo a chemical change, allowing selective removal of either the exposed or unexposed regions, depending on whether a positive or negative resist is used.

The resolution of EBL can reach down to the nanometer scale, making it invaluable for applications that require high precision, such as the fabrication of integrated circuits, photonic devices, and nanostructures. However, EBL is relatively slow compared to other lithography methods, such as photolithography, which limits its use for mass production. Despite this limitation, its ability to create custom, high-resolution patterns makes it an essential tool in research and development within the fields of microelectronics and nanotechnology.

Hurst Exponent Time Series Analysis

The Hurst Exponent is a statistical measure used to analyze the long-term memory of time series data. It helps to determine the nature of the time series, whether it exhibits a tendency to regress to the mean (H < 0.5), is a random walk (H = 0.5), or shows persistent, trending behavior (H > 0.5). The exponent, denoted as HHH, is calculated from the rescaled range of the time series, which reflects the relative dispersion of the data.

To compute the Hurst Exponent, one typically follows these steps:

  1. Calculate the Rescaled Range (R/S): This involves computing the range of the data divided by the standard deviation.
  2. Logarithmic Transformation: Take the logarithm of the rescaled range and the time interval.
  3. Linear Regression: Perform a linear regression on the log-log plot of the rescaled range versus the time interval to estimate the slope, which represents the Hurst Exponent.

In summary, the Hurst Exponent provides valuable insights into the predictability and underlying patterns of time series data, making it an essential tool in fields such as finance, hydrology, and environmental science.

Borel’S Theorem In Probability

Borel's Theorem is a foundational result in probability theory that establishes the relationship between probability measures and the topology of the underlying space. Specifically, it states that if we have a complete probability space, any countable collection of measurable sets can be approximated by open sets in the Borel σ\sigmaσ-algebra. This theorem is crucial for understanding how probabilities can be assigned to events, especially in the context of continuous random variables.

In simpler terms, Borel's Theorem allows us to work with complex probability distributions by ensuring that we can represent events using simpler, more manageable sets. This is particularly important in applications such as statistical inference and stochastic processes, where we often deal with continuous outcomes. The theorem highlights the significance of measurable sets and their properties in the realm of probability.

Stirling Engine

The Stirling engine is a type of heat engine that operates by cyclic compression and expansion of air or another gas at different temperature levels. Unlike internal combustion engines, it does not rely on the combustion of fuel within the engine itself; instead, it uses an external heat source to heat the gas, which then expands and drives a piston. This process can be summarized in four main steps:

  1. Heating: The gas is heated externally, causing it to expand.
  2. Expansion: As the gas expands, it pushes the piston, converting thermal energy into mechanical work.
  3. Cooling: The gas is then moved to a cooler area, where it loses heat and contracts.
  4. Compression: The piston compresses the cooled gas, preparing it for another cycle.

The efficiency of a Stirling engine can be quite high, especially when operating between significant temperature differences, and it is often praised for its quiet operation and versatility in using various heat sources, including solar energy and waste heat.