So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115

So far simulations used the same EC chain across all participants after
the first instance. This approach was simple but not representative of
the real-world where the EC chain may diverge across multiple subsets
of participants.

The changes here implement a comprehensive set of APIs that allow a set
of EC generation strategies to mix-and-match and replicate complex EC
chain evolution through iterations of a simulation. The chain evolution
is controllable by instance by actor ID. This allows us to simulate
fluctuating EC evolution patterns throughout a simulation. The changes
include an initial set of EC generators that cover:

* Fixed EC for all instances and participants.
* Random EC per instance per participant.
* Random EC per instance but uniform across all participants.
* Common prefix EC per instance with probabilistic length
* Majority common prefix across a set of participants with probabilistic
  length

The EC generators can be aggregated and set per number of participants,
which enables simulation of a flexible set of scenarios including
evolving majority common prefix.

Fixes #115