Shelegia_Motta_2021.IModel
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import platform import re # add typing support for Python 3.5 - 3.7 if re.match("3.[5-7].*", platform.python_version()) is None: from typing import Dict, Final else: from typing import Dict from typing_extensions import Final import abc import matplotlib.axes class IModel: """ Interface for all models in Shelegia and Motta (2021). """ def __init__(self): self.ENTRANT_CHOICES: Final[Dict[str, str]] = {"complement": "C", "substitute": "S", "indifferent": "I"} """ Contains all the possible product choices of the entrant. - complement (C): The entrant develops another complement for the primary product of the incumbent. - substitute (S): The entrant develops a perfect substitute to the primary product of the incumbent. - indifferent (I): The entrant is indifferent between the two options, mentioned above. """ self.INCUMBENT_CHOICES: Final[Dict[str, str]] = {"copy": "©", "refrain": "Ø"} """ Contains all the possible answers of the incumbent to the choice of the entrant. - copy (©): The incumbent copies the complement of the entrant. - refrain (Ø): The incumbent does not take any action. """ self.DEVELOPMENT_OUTCOME: Final[Dict[str, str]] = {"success": "Y", "failure": "N"} """ Contains all the possible outcomes of the development for the chosen product of the entrant or the merged entity. - success (Y): The entrant has sufficient assets to develop the second product. - failure (N): The entrant has not sufficient assets to develop the second product. """ @abc.abstractmethod def _calculate_copying_fixed_costs_values(self) -> Dict[str, float]: """ Calculates the thresholds for the fixed costs for copying of the incumbent. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the fixed costs for copying of the incumbent. """ pass @abc.abstractmethod def _calculate_asset_values(self) -> Dict[str, float]: """ Calculates the thresholds for the assets of the entrant. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the assets of the entrant. """ pass @abc.abstractmethod def _calculate_payoffs(self) -> Dict[str, Dict[str, float]]: """ Calculates the payoffs for different market configurations. Includes the following payoffs: - pi(I): - pi(E): - CS: - W: Returns ------- Dict[str, float] Includes the mentioned payoffs for different market configurations. """ pass @abc.abstractmethod def get_asset_values(self) -> Dict[str, float]: """ Returns the asset thresholds of the entrant. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the assets of the entrant. """ pass @abc.abstractmethod def get_copying_fixed_costs_values(self) -> Dict[str, float]: """ Returns the fixed costs for copying thresholds of the incumbent. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the fixed costs for copying of the incumbent. """ pass @abc.abstractmethod def get_payoffs(self) -> Dict[str, Dict[str, float]]: """ Returns the payoffs for different market configurations. A market configuration can include: - $I_P$ : Primary product sold by the incumbent. - $I_C$ : Complementary product to $I_P$ potentially sold by the incumbent, which is copied from $E_C$. - $E_P$ : Perfect substitute to $I_P$ potentially sold by the entrant. - $E_C$ : Complementary product to $I_P$ currently sold by the entrant - $\\tilde{E}_C$ : Complementary product to $I_P$ potentially sold by the entrant. <br> | Market Config. | $\pi(I)$ | $\pi(E)$ | CS | W | |-----------------------|:--------:|:--------:|:--:|:-:| | $I_P$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | <br> The payoffs are specific to the models. Returns ------- Dict[str, Dict[str, float]] Contains the mentioned payoffs for different market configurations. """ @abc.abstractmethod def get_optimal_choice(self, A: float, F: float) -> Dict[str, str]: """ Returns the optimal choice of the entrant and the incumbent based on a pair of assets of the entrant and fixed costs for copying of the incumbent. The output dictionary will contain the following details: - "entrant": choice of the entrant (possible choices listed in Shelegia_Motta_2021.IModel.IModel.ENTRANT_CHOICES)) - "incumbent": choice of the incumbent (possible choices listed in Shelegia_Motta_2021.IModel.IModel.INCUMBENT_CHOICES) - "development": outcome of the development (possible outcomes listed in Shelegia_Motta_2021.IModel.IModel.DEVELOPMENT_OUTCOME) To understand the details of the logic implemented, consult the chapter in Shelegia and Motta (2021) corresponding to the model. Parameters ---------- A : float Assets of the entrant. F : float Fixed costs for copying of the incumbent. Returns ------- Dict[str, str] Optimal choice of the entrant, the incumbent and the outcome of the development. """ pass @abc.abstractmethod def plot_incumbent_best_answers(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the best answers of the incumbent to all possible actions of the entrant. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the best answers plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def plot_equilibrium(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the equilibrium path based on the choices of the entrant and incumbent. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the equilibrium plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def plot_payoffs(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the payoffs for different market configurations. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the payoff plot.<br> - legend: If false, all legends are turned off.<br> - products_legend: If true, a legend, containing all possible products of the entrant and the incumbent, will be added to the plot.<br> - opacity : Opacity of the not optimal payoffs. (floating number between 0 and 1)<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def __str__(self) -> str: """ Returns a string representation of the object. Includes: - Asset thresholds of the entrant - Fixed costs for copying thresholds of the incumbent. - Payoffs for different market configurations for all stakeholders Returns ------- String String representation of the object. """ pass
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class IModel: """ Interface for all models in Shelegia and Motta (2021). """ def __init__(self): self.ENTRANT_CHOICES: Final[Dict[str, str]] = {"complement": "C", "substitute": "S", "indifferent": "I"} """ Contains all the possible product choices of the entrant. - complement (C): The entrant develops another complement for the primary product of the incumbent. - substitute (S): The entrant develops a perfect substitute to the primary product of the incumbent. - indifferent (I): The entrant is indifferent between the two options, mentioned above. """ self.INCUMBENT_CHOICES: Final[Dict[str, str]] = {"copy": "©", "refrain": "Ø"} """ Contains all the possible answers of the incumbent to the choice of the entrant. - copy (©): The incumbent copies the complement of the entrant. - refrain (Ø): The incumbent does not take any action. """ self.DEVELOPMENT_OUTCOME: Final[Dict[str, str]] = {"success": "Y", "failure": "N"} """ Contains all the possible outcomes of the development for the chosen product of the entrant or the merged entity. - success (Y): The entrant has sufficient assets to develop the second product. - failure (N): The entrant has not sufficient assets to develop the second product. """ @abc.abstractmethod def _calculate_copying_fixed_costs_values(self) -> Dict[str, float]: """ Calculates the thresholds for the fixed costs for copying of the incumbent. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the fixed costs for copying of the incumbent. """ pass @abc.abstractmethod def _calculate_asset_values(self) -> Dict[str, float]: """ Calculates the thresholds for the assets of the entrant. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the assets of the entrant. """ pass @abc.abstractmethod def _calculate_payoffs(self) -> Dict[str, Dict[str, float]]: """ Calculates the payoffs for different market configurations. Includes the following payoffs: - pi(I): - pi(E): - CS: - W: Returns ------- Dict[str, float] Includes the mentioned payoffs for different market configurations. """ pass @abc.abstractmethod def get_asset_values(self) -> Dict[str, float]: """ Returns the asset thresholds of the entrant. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the assets of the entrant. """ pass @abc.abstractmethod def get_copying_fixed_costs_values(self) -> Dict[str, float]: """ Returns the fixed costs for copying thresholds of the incumbent. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the fixed costs for copying of the incumbent. """ pass @abc.abstractmethod def get_payoffs(self) -> Dict[str, Dict[str, float]]: """ Returns the payoffs for different market configurations. A market configuration can include: - $I_P$ : Primary product sold by the incumbent. - $I_C$ : Complementary product to $I_P$ potentially sold by the incumbent, which is copied from $E_C$. - $E_P$ : Perfect substitute to $I_P$ potentially sold by the entrant. - $E_C$ : Complementary product to $I_P$ currently sold by the entrant - $\\tilde{E}_C$ : Complementary product to $I_P$ potentially sold by the entrant. <br> | Market Config. | $\pi(I)$ | $\pi(E)$ | CS | W | |-----------------------|:--------:|:--------:|:--:|:-:| | $I_P$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | <br> The payoffs are specific to the models. Returns ------- Dict[str, Dict[str, float]] Contains the mentioned payoffs for different market configurations. """ @abc.abstractmethod def get_optimal_choice(self, A: float, F: float) -> Dict[str, str]: """ Returns the optimal choice of the entrant and the incumbent based on a pair of assets of the entrant and fixed costs for copying of the incumbent. The output dictionary will contain the following details: - "entrant": choice of the entrant (possible choices listed in Shelegia_Motta_2021.IModel.IModel.ENTRANT_CHOICES)) - "incumbent": choice of the incumbent (possible choices listed in Shelegia_Motta_2021.IModel.IModel.INCUMBENT_CHOICES) - "development": outcome of the development (possible outcomes listed in Shelegia_Motta_2021.IModel.IModel.DEVELOPMENT_OUTCOME) To understand the details of the logic implemented, consult the chapter in Shelegia and Motta (2021) corresponding to the model. Parameters ---------- A : float Assets of the entrant. F : float Fixed costs for copying of the incumbent. Returns ------- Dict[str, str] Optimal choice of the entrant, the incumbent and the outcome of the development. """ pass @abc.abstractmethod def plot_incumbent_best_answers(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the best answers of the incumbent to all possible actions of the entrant. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the best answers plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def plot_equilibrium(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the equilibrium path based on the choices of the entrant and incumbent. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the equilibrium plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def plot_payoffs(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the payoffs for different market configurations. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the payoff plot.<br> - legend: If false, all legends are turned off.<br> - products_legend: If true, a legend, containing all possible products of the entrant and the incumbent, will be added to the plot.<br> - opacity : Opacity of the not optimal payoffs. (floating number between 0 and 1)<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass @abc.abstractmethod def __str__(self) -> str: """ Returns a string representation of the object. Includes: - Asset thresholds of the entrant - Fixed costs for copying thresholds of the incumbent. - Payoffs for different market configurations for all stakeholders Returns ------- String String representation of the object. """ pass
Interface for all models in Shelegia and Motta (2021).
View Source
def __init__(self): self.ENTRANT_CHOICES: Final[Dict[str, str]] = {"complement": "C", "substitute": "S", "indifferent": "I"} """ Contains all the possible product choices of the entrant. - complement (C): The entrant develops another complement for the primary product of the incumbent. - substitute (S): The entrant develops a perfect substitute to the primary product of the incumbent. - indifferent (I): The entrant is indifferent between the two options, mentioned above. """ self.INCUMBENT_CHOICES: Final[Dict[str, str]] = {"copy": "©", "refrain": "Ø"} """ Contains all the possible answers of the incumbent to the choice of the entrant. - copy (©): The incumbent copies the complement of the entrant. - refrain (Ø): The incumbent does not take any action. """ self.DEVELOPMENT_OUTCOME: Final[Dict[str, str]] = {"success": "Y", "failure": "N"} """ Contains all the possible outcomes of the development for the chosen product of the entrant or the merged entity. - success (Y): The entrant has sufficient assets to develop the second product. - failure (N): The entrant has not sufficient assets to develop the second product. """
Contains all the possible product choices of the entrant.
- complement (C): The entrant develops another complement for the primary product of the incumbent.
- substitute (S): The entrant develops a perfect substitute to the primary product of the incumbent.
- indifferent (I): The entrant is indifferent between the two options, mentioned above.
Contains all the possible answers of the incumbent to the choice of the entrant.
- copy (©): The incumbent copies the complement of the entrant.
- refrain (Ø): The incumbent does not take any action.
Contains all the possible outcomes of the development for the chosen product of the entrant or the merged entity.
- success (Y): The entrant has sufficient assets to develop the second product.
- failure (N): The entrant has not sufficient assets to develop the second product.
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@abc.abstractmethod def get_asset_values(self) -> Dict[str, float]: """ Returns the asset thresholds of the entrant. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the assets of the entrant. """ pass
Returns the asset thresholds of the entrant.
Number and type of the thresholds will be specific to the model.
Returns
- Dict[str, float]: Includes the thresholds for the assets of the entrant.
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@abc.abstractmethod def get_copying_fixed_costs_values(self) -> Dict[str, float]: """ Returns the fixed costs for copying thresholds of the incumbent. Number and type of the thresholds will be specific to the model. Returns ------- Dict[str, float] Includes the thresholds for the fixed costs for copying of the incumbent. """ pass
Returns the fixed costs for copying thresholds of the incumbent.
Number and type of the thresholds will be specific to the model.
Returns
- Dict[str, float]: Includes the thresholds for the fixed costs for copying of the incumbent.
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@abc.abstractmethod def get_payoffs(self) -> Dict[str, Dict[str, float]]: """ Returns the payoffs for different market configurations. A market configuration can include: - $I_P$ : Primary product sold by the incumbent. - $I_C$ : Complementary product to $I_P$ potentially sold by the incumbent, which is copied from $E_C$. - $E_P$ : Perfect substitute to $I_P$ potentially sold by the entrant. - $E_C$ : Complementary product to $I_P$ currently sold by the entrant - $\\tilde{E}_C$ : Complementary product to $I_P$ potentially sold by the entrant. <br> | Market Config. | $\pi(I)$ | $\pi(E)$ | CS | W | |-----------------------|:--------:|:--------:|:--:|:-:| | $I_P$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | | $I_P + I_C$ ; $E_C + \\tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. | <br> The payoffs are specific to the models. Returns ------- Dict[str, Dict[str, float]] Contains the mentioned payoffs for different market configurations. """
Returns the payoffs for different market configurations.
A market configuration can include:
- $I_P$ : Primary product sold by the incumbent.
- $I_C$ : Complementary product to $I_P$ potentially sold by the incumbent, which is copied from $E_C$.
- $E_P$ : Perfect substitute to $I_P$ potentially sold by the entrant.
- $E_C$ : Complementary product to $I_P$ currently sold by the entrant
- $\tilde{E}_C$ : Complementary product to $I_P$ potentially sold by the entrant.
| Market Config. | $\pi(I)$ | $\pi(E)$ | CS | W |
|---|---|---|---|---|
| $I_P$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. |
| $I_P + I_C$ ; $E_C$ | N.A. | N.A. | N.A. | N.A. |
| $I_P$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. |
| $I_P + I_C$ ; $E_P + E_C$ | N.A. | N.A. | N.A. | N.A. |
| $I_P$ ; $E_C + \tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. |
| $I_P + I_C$ ; $E_C + \tilde{E}_C$ | N.A. | N.A. | N.A. | N.A. |
The payoffs are specific to the models.
Returns
- Dict[str, Dict[str, float]]: Contains the mentioned payoffs for different market configurations.
View Source
@abc.abstractmethod def get_optimal_choice(self, A: float, F: float) -> Dict[str, str]: """ Returns the optimal choice of the entrant and the incumbent based on a pair of assets of the entrant and fixed costs for copying of the incumbent. The output dictionary will contain the following details: - "entrant": choice of the entrant (possible choices listed in Shelegia_Motta_2021.IModel.IModel.ENTRANT_CHOICES)) - "incumbent": choice of the incumbent (possible choices listed in Shelegia_Motta_2021.IModel.IModel.INCUMBENT_CHOICES) - "development": outcome of the development (possible outcomes listed in Shelegia_Motta_2021.IModel.IModel.DEVELOPMENT_OUTCOME) To understand the details of the logic implemented, consult the chapter in Shelegia and Motta (2021) corresponding to the model. Parameters ---------- A : float Assets of the entrant. F : float Fixed costs for copying of the incumbent. Returns ------- Dict[str, str] Optimal choice of the entrant, the incumbent and the outcome of the development. """ pass
Returns the optimal choice of the entrant and the incumbent based on a pair of assets of the entrant and fixed costs for copying of the incumbent.
The output dictionary will contain the following details:
- "entrant": choice of the entrant (possible choices listed in Shelegia_Motta_2021.IModel.IModel.ENTRANT_CHOICES))
- "incumbent": choice of the incumbent (possible choices listed in Shelegia_Motta_2021.IModel.IModel.INCUMBENT_CHOICES)
- "development": outcome of the development (possible outcomes listed in Shelegia_Motta_2021.IModel.IModel.DEVELOPMENT_OUTCOME)
To understand the details of the logic implemented, consult the chapter in Shelegia and Motta (2021) corresponding to the model.
Parameters
- A (float): Assets of the entrant.
- F (float): Fixed costs for copying of the incumbent.
Returns
- Dict[str, str]: Optimal choice of the entrant, the incumbent and the outcome of the development.
#
@abc.abstractmethod
def
plot_incumbent_best_answers(
self,
axis: matplotlib.axes._axes.Axes = None,
**kwargs
) -> matplotlib.axes._axes.Axes:
View Source
@abc.abstractmethod def plot_incumbent_best_answers(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the best answers of the incumbent to all possible actions of the entrant. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the best answers plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass
Plots the best answers of the incumbent to all possible actions of the entrant.
Parameters
- axis (matplotlib.axes.Axes): Axis to draw the plot on. (optional)
- **kwargs: Optional key word arguments for the best answers plot.
- title: title on top of the plot, instead of the default title.
- legend: If false, all legends are turned off.
- options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.
- asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.
- costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.
- legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).
- x_max : Maximum number plotted on the x - axis.
- y_max : Maximum number plotted on the y - axis.
- title: title on top of the plot, instead of the default title.
Returns
- matplotlib.axes.Axes: Axis containing the plot.
#
@abc.abstractmethod
def
plot_equilibrium(
self,
axis: matplotlib.axes._axes.Axes = None,
**kwargs
) -> matplotlib.axes._axes.Axes:
View Source
@abc.abstractmethod def plot_equilibrium(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the equilibrium path based on the choices of the entrant and incumbent. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the equilibrium plot.<br> - title: title on top of the plot, instead of the default title.<br> - legend: If false, all legends are turned off.<br> - options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.<br> - asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.<br> - costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.<br> - legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).<br> - x_max : Maximum number plotted on the x - axis.<br> - y_max : Maximum number plotted on the y - axis.<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass
Plots the equilibrium path based on the choices of the entrant and incumbent.
Parameters
- axis (matplotlib.axes.Axes): Axis to draw the plot on. (optional)
- **kwargs: Optional key word arguments for the equilibrium plot.
- title: title on top of the plot, instead of the default title.
- legend: If false, all legends are turned off.
- options_legend: If true, an additional legend, explaining the options of the entrant and the incumbent, will be added to the plot.
- asset_legend: If true, an additional legend explaining the thresholds of the assets of the entrant will be added to the plot.
- costs_legend: If true, an additional legend explaining the thresholds of the fixed costs of copying for the incumbent will be added to the plot.
- legend_width : Maximum number of characters in one line in the legend (for adjustments to figure width).
- x_max : Maximum number plotted on the x - axis.
- y_max : Maximum number plotted on the y - axis.
- title: title on top of the plot, instead of the default title.
Returns
- matplotlib.axes.Axes: Axis containing the plot.
#
@abc.abstractmethod
def
plot_payoffs(
self,
axis: matplotlib.axes._axes.Axes = None,
**kwargs
) -> matplotlib.axes._axes.Axes:
View Source
@abc.abstractmethod def plot_payoffs(self, axis: matplotlib.axes.Axes = None, **kwargs) -> matplotlib.axes.Axes: """ Plots the payoffs for different market configurations. Parameters ---------- axis : matplotlib.axes.Axes Axis to draw the plot on. (optional) **kwargs Optional key word arguments for the payoff plot.<br> - legend: If false, all legends are turned off.<br> - products_legend: If true, a legend, containing all possible products of the entrant and the incumbent, will be added to the plot.<br> - opacity : Opacity of the not optimal payoffs. (floating number between 0 and 1)<br> Returns ------- matplotlib.axes.Axes Axis containing the plot. """ pass
Plots the payoffs for different market configurations.
Parameters
- axis (matplotlib.axes.Axes): Axis to draw the plot on. (optional)
- **kwargs: Optional key word arguments for the payoff plot.
- legend: If false, all legends are turned off.
- products_legend: If true, a legend, containing all possible products of the entrant and the incumbent, will be added to the plot.
- opacity : Opacity of the not optimal payoffs. (floating number between 0 and 1)
- legend: If false, all legends are turned off.
Returns
- matplotlib.axes.Axes: Axis containing the plot.